dataesr/scientific-paragraphs-categorization huggingface dataset:
This work revisits set reconciliation, the problem of synchronizing two multisets of xedsize values while minimizing transmission complexity. We propose a new number-theoretic reconciliation protocol called Divide and Factor (D&F) that achieves optimal asymptotic transmission complexity as do previously known alternative algorithms. We analyze the computational complexities of various D&F variants, study the problem of synchronizing sets of variable-size les using hash functions and apply D&F to synchronize le hierarchies taking le locations into account. We describe btrsync, our open-source D&F implementation, and benchmark it against the popular software rsync. It appears that btrsync transmits much less data than rsync, at the expense of a relatively modest computational overhead.
In non-trivial tasks, however, btrsync outperforms rsync. This is the case of the syn datasets, where btrsync does not have to transfer information about all unmodied les, and even more so in the case where there are no modications at all. For Firefox source code datasets, btrsync saves a very small amount of bandwidth, presumably because of unmodied les. For the btrsync source code dataset, we notice that btrsync, unlike rsync, was able to detect the move and avoid retransferring the moved folder.
Possible Renements. The previous analyses are incomplete because of the following approximations: We used a xed u in all rounds. Nothing forbids using a dierent u at each iteration, or even ne-tuning the u 's adaptively as a function of the laundry's eect on the progressively reconciliated multisets. Our analysis treats t as a constant, but large t values increase p and hence the number of potential les detected as dierent per iteration -an eect disregarded supra. A dierent approach is to optimize t and u experimentally, e. g. , using the open source D&F program btrsync developed by the authors (cf. Section 8).
e. g. , if the p k are powers of two then hashing into odd integers might suce. This would make reconciliation harder because multiple factorizations of a and b as products of h i and h i could exist while only one of them would be the correct one. Entities and Datasets Transmission (Bytes) Time (s) Neil's F Oscar's F TXrs RXrs TXbt RXbt δbt -δrs δbt δrs trs tbt source empty 778k 2k 780k 10k 10k 1. 0 0. 1 0. 7 empty source 24 12 12k 6k 18k 496. 6 0. 0 0. 4 empty empty 24 12 19 30 13 1. 4 0. 0 0. 3 syn syn_shuf 55k 19k 7k 3k -63k 0. 1 0. 5 0. 8 syn_shuf syn 54k 19k 7k 3k -63k 0. 1 0. 2 0. 8 syn syn 55k 19k 327 30 -73k 0. 0 0. 5 0. 7 ff-13. 0. 1 ff-13. 0 41M 1k 40M 3k -1M 1. 0 1. 6 8. 1 source_moved source 778k 1k 3k 2k -775k 0. 0 0. 1 0. 4 choices.
Building complex, incremental, data-driven modular models in NetLogo  [28] , for instance, is a difficult task. And it becomes even more difficult when different data sources, at different levels of representation, are to be used. Interpreting these models is also very tedious, given the lack of flexibility offered by the existing platforms in the visualization and parametrization of the models. While some offers have tried to overcome these limitations (e. g. Repast  [23] , Mason  [20] ), they have failed until now to propose a credible alternative in terms of modeling platforms. Repast, for instance, is more a well designed toolbox than a platform as it requires modelers to be highly proficient in Java programming and Eclipse development, which is rarely the case in the scientific domains targeted by ABM.
Similarly, these proposals also fall short in terms of simulation platform, as designing virtual experiments that combine easy parametrization and highlevel interactive visualization is as complicated as building the model itself and requires the use of several external tools. The GAMA 6 modeling and simulation platform  [9]  [26], developed since 2007 as an open-source project, aims at overcoming these lacks by providing modelers -which are not, most of the time, computer scientists -with tools to develop and experiment highly complex models through a well-thought integration of agent-based programming, geographical data management, flexible visualization tools and multi-level representation. GAMA provides a complete modeling language (GAma Modeling Language) and an integrated development environment that allows modelers to build models as quickly and easily as in NetLogo while going beyond what Repast or Mason offer in terms of simulated experiments.
It is currently applied in several projects in environmental decision-support systems, urban design, water management, biological invasions, climate change adaptation or disaster mitigation. This paper aims at presenting the new features developed in the latest version of GAMA. We will focus in particular on the following features: the evolution of the GAML language for specifying models using multiple paradigms (especially combinations of mathematical and computer approaches), the seamless integration of geographical data and other spatial data, the tools offered to support high-level visualization of simulations and the multi-level description of models.
GAMA Meta-Model The concepts and the operational semantic of GAML are completely described in a meta-model (see Figure  1 ) from which every model written in the language derives. This meta-model supports the development of multi-level agent-based models by considering three main sets of abstract classes that represent, respectively, the entities, the space and the time of a model. -The representation of Entities are described by two main classes: • Agent represents an individual entity of a simulation, which is conceptually similar to an object in an object-oriented language. It represents a simulated entity in the simulation. • Population represents a group of agents sharing the same structure and behavior in a simulation. The population is responsible for managing the agents it is composed of.
The GAML modeling language In order to ease the work of modelers, GAMA provides a complete IDE (Integrated Development Environment) supporting the definition of models. In particular, the IDE, based on Eclipse and XText, provides modern features such as auto-compilation, auto-completion, quick fixes, text coloring, etc. Modelers define their models using the GAML language. The interest of providing a DSL (Domain Specific Language) to modelers is to ease the definition of models. Indeed, the GAML language is very simple to use, even by modelers that have low programming skills. GAML is an agent-oriented language. Modelers define species of agents, i. e. archetype of agents, their characteristics, behaviors and aspects. It is a typed language, variables (static or dynamic) has to be typed. The behaviors of agents are defined through actions and reflexes.
Indeed, GAMA allows to use geographic vector data to create agents, each object of the geographical data will be automatically used to instantiate an agent, GAMA taking care of managing the spatial projection of the data and, if necessary, of reading the values of the attributes. In Figure  3 , we present an extension of the model presented in Figure  2  in which roads and buildings agents are created from shapefile. Each object of the shapefile then becomes an agent: a roads agent for the road shapefile and a buildings agent for the building shapefile. At initialization, the people agents are located on one of the building agent randomly chosen. GAMA provides as well many advanced spatial operators (spatial queries, union, difference, intersection. ) that are directly usable through the GAML language.
In GAMA, 3D is fully integrated to agent based modeling paradigm with a dedicated language that handle high-level representation  [14]. The 3D visualization library in GAMA 1. 6 uses OpenGL via the Java Binding for the OpenGL (JOGL) API library. GAMA offers a generic framework to separate the visual representation of the model from the underlying model, allowing different visual representations at different levels of abstraction, and insures that the visualization of a simulation and the interaction on a simulation are independent processes that do not alter the reference model itself  [13]. In this approach a view is seen as a model on which one can represent, abstract and interact without changing the definition of the observed model. GAML language enables to define specific rich aspect by using operation such as repetition, modularity and recursion above the definition of graphical primitives.
In this example, a building agent controls the infection propagation of its micro-agents using a simple mathematical formula. In real application, we can integrate a SIR model to the building agent in order to control the infection process. A person stays in a building for a certain amount of time. After that the building agent decides to release this person and the person continues to move in the road network. The modeler again uses the morphogenesis operation (release GAML statement), to change the representation level of people from people in building to people. Since the beginning of its development in 2007, GAMA has been deeply enriched. In particular, the new 1. 6 version provides many powerful features to manage GIS data, advanced visualization and multi-level models. Many others features that were not presented in this paper are also available: database management (SQL), connexion to R, a 3D-physics engine plug-in.
Acknowledgments The authors would like to thank  C. Grataloup  for participating in building the lists of words,  V. Monatte  and the participants for their collaboration during testing. Michel Hoen  and  Joshua Birchall  for discussions and corrections on the paper. And the team of Musikit shop in Lyon for their advice in the choice of the experimental equipment. Finally, the members of the  Linguistic Division of the Museu Goeldi (MPEG/MCT, Belem, Brazil)  are also thanked for their constant support.
Acknowledgement We would like to thank the  French Ministry for the Economy, Industry and Employment (DGCIS)  for the support in the  PEGASE  project. We would like also to thank  G. Dalla Costa ,  A. Bissay ,  J. Depoil ,  L. Kepka ,  S. El Kadiri ,  L. Michea  and  M. Zrouki  for their great help in developing this platform.
Acknowledgments The authors would like to thank several key players of the SPHERE instrument, namely  Thierry Fusco ,  David Mouillet ,  Jean-Luc Beuzit  and  Marc Ferrari  for simulating discussions and support, and the  Région Provence-Alpes-Côte d'Azur  for partial financial support of  B. Paul 's scholarship. This work was partly funded by the  European Commission  under  FP7  Grant Agreement No. 312430   Optical Infrared Coordination Network for Astronomy.
OUTLINES OF AN OPEN HARDWARE MOVEMENT The surge of homebrewed digital fabrication tools today is in continuation with a longer trajectory of open hardware development. As with so many other things today, good or bad, this trend can be traced back to the small-is-beautiful philosophy that flourished in the 1960s American counterculture, culminating in the legendary Homebrew Computer Club  (Levy 1984; Flichy 2007). Two forerunners to the movement around open hardware in the late 1990s and early 2000 were the Open Cores project -although it occupied a middle ground between software and hardware development, and the short-lived Simputer project -an initiative in India to produce a computer more suitable for developing countries  (Seaman, 2001). Arguably, the first project that vindicated the methods and licensing schemes of free software development, applied those practices to open hardware development, and pulled off a state-ofthe-art technology without any backing from universities or firms, was the Ronja project.
Acknowledgements. Part of this work has been carried out during a visit of P. G. K. to laboratoire  Jean Kuntzmann , supported by the  CNRS, France , to which P. G. K. is grateful for the hospitality. The authors are grateful to  Nicholas Boechler  for helpful inputs on experimental issues. G. J. acknowledges stimulating discussions with  E. Dumas ,  B. Bidegaray ,  M. Peyrard  and  G. Theocharis. J. C. acknowledges financial support from the  MICINN  project  FIS2008-04848. P. G. K. also acknowledges support from the  US National Science Foundation  through grant  CMMI-1000337  and also from the  Alexander S. Onassis Public Benefit Foundation  through the grant  RZG 003/2010-2011.
Statistical Shape Model of Legendre Moments In the method proposed here the prior shape constraint is introduced into the segmentation process in the form of probability density function defined on the low dimensional shape space  [4]  and estimated using Parzen window method. The shape space is constructed using PCA method on a training set consisting of N s binary silhouette images with foreground and background represented respectively by ones and zeros. The training data can be obtained from previously segmented images or generated from computer models of the objects of interest. In the first instance the centralnormalized Legendre moments {λ i } N s i=1 are calculated for the shapes {Ω i } N s i=1 from the training database. Following the methodology proposed in  [4]  the mean vector λ and the N f × N f covariance matrix Q are estimated using: λ = 1 N s N s i=1 λ i (7) Q = 1 N s N s i=1 (λ i -λ)(λ i -λ) T (8) Subsequently the N f × N c projection matrix P is formed by the eigenvectors of the covariance matrix Q that correspond to the largest N c (N c ≤ min{N s , N f }) eigenvalues.
Silhouette Data A first set of experiments were carried out using a chicken image set consisting of 20 binary silhouette images of different shapes, orientations and sizes from the MPEG7 CE shape-1 Part B database  [15]. The first 19 of them were used as training shapes for building the statistical prior model and the remaining image was used for testing (see Fig. 2 ). The diversity of the training shapes can be clearly notedrotations in the images were not removed on purpose to test robustness of the proposed method against large shape variability. Shape variations represented by the sample points along the three most dominant principal axes in the feature space are shown in Fig. 3 , from which it can be observed that the three principal axes respectively capture the shape variabilities of rotation (vertical position v.
From left to right, each column corresponds to different magnitude (-2, -1, 0, 1, 2 times the squared root of the eigenvalue associated with the corresponding eigenvector) of shape variations from the mean shape in the noisy image. For the structural noise (Fig. 4(c) ), hard alterations are made on the original image in order to emphasis the need for shape constraints. Finally, the last test image is corrupted by both Gaussian and structural noise (Fig. 4(d) ). All the experimental results shown in Fig. 5  were based on the test images as shown in Fig. 4  and with the same parameters N o = 40 and N c = 10, used to calculate Legendre moments and the shape space. As it can be seen in Fig. 5  for each corrupted image, the proposed method makes a satisfactory shape segmentation even though this shape was not included in the database of the shapes used to calculate the shape space.
Gray Scale Images Finally experiments were carried out using a gray scale images to test performance of the proposed methodology on real images. The first test image used in these experiments is shown in Fig. 13 (a) where the objective was to segment the cup. The shape space was constructed from the image set shown in Fig. 12 , with a subset of the MPEG7 CE shape-1 Part B database used. It can be clearly seen that the training shapes integrate a large shape variability, and that differ-Fig. 13  Segmentation results for the cup image (a) an image to be segmented, (b) result of segmentation using Chan-Vese model, (c) result of the segmentation using the multi-reference method from  [9] , (d) result of the segmentation using the proposed method Fig. 14  Trajectory in the space of the first two principal directions of the cup shape space corresponding to result shown in Fig.
The second test image used in the experiments is shown in Fig. 16 , where the objective is to segment the garden gnome figurine. This time the training data was obtained from a video sequence showing the same figurine rotating against a black background. The silhouettes used for training are shown in Fig. 15. These were extracted from 49 frames, sampled from a video sequence covering approximately 320 • of the rotational viewing angle of the figurine. Figure  17  shows corresponding shape evolution in the feature space. It could be noticed that although contour was initially evolving around shapes shown in the bottom row of Fig. 15 , represented in the top left part of the feature space, it was eventually able to cross a "long valley" in the feature space and converge to a shape in the vicinity of the shapes shown in the top row of Fig.
HomePlug AV2 is the solution identified by the HomePlug Alliance to achieve the improved data rate performance required by the new generation of multimedia applications without the need to install extra wires. Developed by industry-leading participants in the HomePlug AV Technical Working Group, the HomePlug AV2 technology provides Gigabit-class connection speeds over the existing AC wires within home. It is designed to meet the market demands for the full set of future in-home networking connectivity. Moreover, HomePlug AV2 guarantees backward interoperability with other HomePlug systems. In this paper, the HomePlug AV2 system architecture is introduced and the technical details of the key features at both the PHY and MAC layers are described. The HomePlug AV2 performance is assessed, through simulations reproducing real home scenarios.
The field results obtained by the AV TWG validated the HomePlug AV2 claimed performance both in terms of achievable data rate and coverage. In this paper, we highlight the key differentiating Home-Plug AV2 features compared to the HomePlug AV technology. At the physical (PHY) layer HomePlug AV2 includes the following. (i) Multiple-Input Multiple-Output (MIMO) signaling with Beamforming to offer the benefit of improved coverage throughout the home, especially on highly attenuated channels. MIMO enables HomePlug AV2 devices to transmit on any two-wire pairs within three-wire configurations comprising Line (L), Neutral (N) and Protective Earth (PE) (the coupling is done in the MIMO Analog Front End (AFE) blocks in Figure  1 ). (ii) Extended Frequency Band up to 86 MHz to increase the throughput especially at the low to mid coverage percentages (part of IFFT/FFT (1024, 8192) and Mapper blocks in Figure  1 ).
The MAC receives MAC Service Data Units (MSDUs) and encapsulates them with a header, optional Arrival Time Stamp (ATS), and a checksum to create a stream of MAC frames. The stream is then divided into 512octet segments, encrypted and encapsulated into serialized PHY Blocks (PBs), and packed as MAC Protocol Data Units (MPDUs) to the PHY unit which then generates the final PHY Protocol Data Unit (PPDU) to be transmitted onto the power line  [2]. HomePlug AV2 enhances significantly the capability above to accommodate new generation of multimedia applications with Gigabit performance.
2-Transmit channels A block diagram of the PHY layer is shown in Figure  1. The HomePlug AV2 system is capable of supporting two network operating modes: AV-only mode and Hybrid mode (1. 0. 1 Frame Control Encoder in Figure  1 ). Hybrid mode is used for coexistence with HomePlug 1. 0. 1 stations. For that purpose, a 1. 0. 1 Frame Control Encoder is included for Hybrid modes and an AV2 Frame Control Encoder for both Hybrid and AV-only modes. The AV-only mode is used for communications in networks where only HomePlug AV and HomePlug AV2 stations are involved. Besides, two OFDM paths are shown in order to implement the MIMO capabilities with 2 transmission ports. Apart from the Hybrid or AV-only Frame Control symbols, the payload data can be sent using adaptive bit loading per carrier or robust modes (ROBO) with fixed Quadrature Phase Shift Keying (QPSK) constellation and several copies of data interleaved in both time and frequency.
At the receiver, an AFE with one, two, three, or four (  ) receive ports operates with individual Automatic Gain Control (AGC) modules and one or more time-synchronization modules to feed separate Frame Control and payload data recovery circuits. Receivers plugged to power outlets which are connected to the three wires Line, Neutral, and Protective Earth might utilize up to three differential mode Rx ports and one common mode Rx port. The Frame Control data is recovered by processing the received signals through a 1024-point FFT (for HomePlug 1. 0. 1 delimiters) and multiple 8192-point FFTs, and through separate Frame Control Decoders for the HomePlug AV2/AV and HomePlug 1. 0. 1 modes. The payload portion of the sampled time domain waveform, which contains only HomePlug AV2 formatted symbols, is processed through the multiple 8192-point FFT (one for each receive port), a MIMO Equalizer that receives   signals, performs receive Beamforming, and recovers the two transmit streams, two Demodulators, a Demultiplexer to combine the two MIMO streams, and a channel deinterleaver followed by a Turbo Convolutional Decoder and a Descrambler to recover the AV2 payload data.
OpenCL computing OpenCL is an open standard for parallel programming of heterogeneous systems  [3]. It provides application programming interfaces for managing hybrid platforms containing many CPUs and GPUs and a programming language based on C99 for writing instructions executed concurrently on the OpenCL devices. OpenCL applications must define an execution model by setting a host program that executes on the host system and send OpenCL kernels to devices using a command queue. A kernel contains executable code that concurrently runs on devices compute units which are called work-items. A memory model need to be explicitly defined to manage data layout in the memory hierarchy. Details of these models such as work-item number in a work-group or memory access pattern have a very strong impact on program efficiency.
Particle method with OpenCL Particle methods have already been implemented on GPU. In  [4] , a two dimensional solver for bluff body flows is developed using OpenGL and CUDA. The method allows to deal with particle distributions of up to 1024 2 at a speed greater than 20 fps. The accuracy of GPU computations was also addressed by comparing GPU results with high resolution double precision benchmark calculations on CPU. Our implementation of the method presented in section 2. 1 uses different abstract layers by means of a Python class hierarchy in order to have a well-defined program structure easy to use and develop. Computations are not performed on the host side of the program but on the devices in different kernels, unnoticed by user. According to the method, the algorithm is split into two parts namely an advection and a remeshing step.
Computational efficiency OpenCL kernels can be compute-bound or memory-bound. Our advection kernel is memory-bound since the operational intensity equals to 2. 25 operations per byte of data accessed from the memory and remeshing kernel is nearly compute-bound with a operational intensity of 9. 5. In figure  3 , we present profiling results and timings. For larger problems, almost all compute time is spent in kernels that could be optimized. The initialization and host code are sequential codes quite independent of the problem size. The initialization part consists in reading problems data and creating python objects structure. The host code tasks are to set the OpenCL execution layout and to launch the kernels. For 256 3 particles, the whole computation is performed in 25 seconds, which corresponds to 1.