dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and Future Work We have presented the DOREMUS resource-a collection of linked RDF datasets representing the catalogs of music works of three major French cultural institutions. The construction of this resource implies the implementation of a processing pipeline that allows for the conversion of the original data to RDF following the DOREMUS ontology, the development, SKOS-ification and alignment of a number of music-specific vocabularies and the interlinking of the datasets, which results in the construction of a reference pivot graph of musical works shared by or unique to the three institutions. This pipeline defines the data production paradigm of DOREMUS that is applicable to other music-library data-the described process is deterministic, extensible, reproducible and documented in numerous pedagogical materials published online.
A number of tools acting at different layers of this pipeline have been introduced: the marc2rdf data converter, the Legato data linking system, the web-interface for SKOS thesauri alignment and mapping validation and enrichment YAM++ on line, as well as the exploratory search engine Overture. We have relied on existing tools where appropriate (matching strings to URI), but the heterogeneity of the input data and the specificity of the librarian practices made this impossible in many cases. In terms of datasets, DOREMUS currently has published (1) three RDF graphs of musical works coming from the BnF, the Philharmonie de Paris and Radio France, (2) a pivot graph currently containing the certain links established automatically between the graphs of musical works, together with the results of the pairwise linking of these graphs, (3) expert curated benchmarks for evaluation of data linking systems, (4) a rich set of music-specific SKOS vocabularies together with their alignments.
We are currently in the process of applying the data conversion and linking workflow to two additional databases from Radio France. Natural Language Processing techniques are being included in the conversion process in order to parse the numerous free-text fields. Overture will soon host all the links between the interlinked works, giving access at the same time to the joined knowledge and to the different information provenances. We have developed a web interface to assist the process of manual validation of links reducing the human effort, which is currently being deployed online. Alignments of our data to established datasets (in particular MusicBrainz) are currently being generated.
Name of resource or tool and description, URL Data bnf: Works and Artists from the BnF, http://data. doremus. org/bnf philharmonie: Works and Concerts from the PP, http://data. doremus. org/philharmonie itema3: Concerts and Recordings from RF, http://data. doremus. org/itema3 DOREMUS triplestore, https://github. com/DOREMUS-ANR/knowledge-base/tree/master/data DOREMUS sparql endpoint, http://data. doremus. org/sparql Example queries, http://data. doremus. org/queries. html DOREMUS ontology, http://data. doremus. org/ontology DOREMUS vocabularies, https://github. com/DOREMUS-ANR/knowledge-base/tree/master/vocabularies Vocab. Alignments, https://github. com/DOREMUS-ANR/knowledge-base/tree/master/vocabularies/alignments DOREMUS linked data, https://github. com/DOREMUS-ANR/knowledge-base/tree/master/linked-data DOREMUS benchmarks 2016, http://islab.
di. unimi. it/content/im_oaei/2016/#doremus DOREMUS benchmarks 2017, http://islab. di. unimi. it/content/im_oaei/2017/#doremus Links to DOREMUS resources and tools. Tools marc2rdf converter, https://github. com/DOREMUS-ANR/marc2rdf itema3 converter, https://github. com/DOREMUS-ANR/itema3converter euterpe converter, https://github. com/DOREMUS-ANR/euterpe-converter Legato: instance matcher, https://github. com/DOREMUS-ANR/legato DOREMUS pivot graph constructor, https://github. com/DOREMUS-ANR/pivot-graph-constructor Overture search engine, http://overture. doremus. org YAM++ vocabulary mapping and validation, http://yamplusplus. lirmm. fr Learning materials, https://github. com/DOREMUS-ANR/training.
This work is organized as follows. In Section 2, we present some prior work related to the data privacy in the smart energy field with a focus on the privacy preserving data mining approaches. Section 3 describes the overall architecture of the classifier as well as the underlying mechanisms for performing the required operations using homomorphic properties. The implementation of the prototype, the dataset description and an evaluation of the associated performance results are given in Section 5. Lastly, the final section gives some insight about future works and perspectives.
The authors in  (Graepel and Naehrig, 2012)  propose a machine learning confidential protocol based on homomorphic encryption in which both training and prediction occur on encrypted data for a Linear Means classifier and a Fisher's Linear Discriminant Classifier. The results on the Wisconsin Breast Cancer dataset (UCI, 2016) show a slow down of 6-7 orders of magnitude when performing on encrypted data instead on plaintexts. For example, using a Linear Means Classifier, the classification of a test vector with 30 attributes takes roughly 6 seconds. In  (Bost et al. , 2014) , only the privacy for the classification process is addressed, by ignoring how the model has been build. As such, they are first conceiving a library with three building blocks which is further used as support for implementing classifiers such as hyperplane decision based, Naive Bayesian, decision trees and AdaBoost.
Load Profiles Dataset and Energy Efficiency Rating In order to reproduce the scenario of the residential district, we have used the CER Smart Metering Project dataset 1. It represents a comprehensive data source as it encompasses several residential load profiles (4225 load profiles in total) with related environmental information. On top of proposing electricity consumption information, the dataset provides specific indications about the householders owners. Subsequently, we chose 40 residential load profiles among the available ones to create our district. The selected profiles have to fulfill the following conditions: • No missing data between January 1st, 2010 and December 31st, 2010. • Householders of the retained load profiles must have completed the information about the surface of the residential building and the number of occupants.
Performances results All the experiments were realized using a standard workstation, with a processor Intel Core I7 at 2. 6 GHz, with 16 GB of RAM memory and Ubuntu 16. 04 as operating system (on 64 bits). The performance tests use a home made C++ implementation of Paillier additive cryptosystem (based on GMP library) as well as HElib  (Halevi, 2013) , the open-source library from IBM, implementing the BGV cryptosystem. For the version based on Paillier, the code has parallelized sections for the encryption part and the distance evaluation (using pragma omp instructions). For the HElib-based tests, we implemented two basic solutions both using batching but one of them being more optimized. Results for Paillier-based prototype Table  1  shows the size, in bytes, of one ciphertext for different security levels (i.
Visual classification does become a real impossible task. One smart way to overcome the problem of visual classification for large amounts of data was the Galaxy Zoo project 1 , where "science citizens" volunteered to classify galaxies through a user-friendly web interface. The first approach was a very simple classification into three types (ETGs, spirals or mergers) but, given the success of the project, a more complex classification system, GZ2, was proposed inWillett et al. 2013. However, galaxy classifications made by amateur astronomers, which is a difficult task even for professionals, has its caveats. For example, features such as bars are only selected when the bar is obvious and the volunteers tend to choose intermediate options when available (e. g. prominence of bulge, roundness, etc.
In particular, CNNs have been proven very successful in the last years for many different image recognition purposes: manuscript numbers, facial identification, etc. (e. g. ,  Ciresan et al. 2012; Russakovsky et al. 2015). CNNs have also been used for morphological classification of galaxies, with a high success rate. The use of these automated classification algorithms has been possible thanks to a series of advances in the last few years: the existence of large number of classified objects needed for the training (thanks to Galaxy Zoo project, in particular), the available computing power and a new set of techniques (e. g. rectified linear units -ReLUs-Nair & Hinton 2010 or dropout regularization,  Hinton et al. 2012 , Srivastava et al. 2014) , as well as open source codes which facilitate the task.
We also simplify the galaxy decision tree by giving only one probability value for each question (see section 4). In addition, we complement the GZ2 classification scheme with a T-Type, trained with the visually classified catalogue from  Nair & Abraham (2010, N10 hereafter). The T-Type is an extremely useful parameter for morphological classification because it gives information about the relative importance of the bulge and disk components by one single number. We also use the N10 catalogue to provide a model to separate pure E from S0's and an alternative bar classification to the GZ2 based one. We provide all these values for the sample of ∼ 670,722 galaxies in the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) Main Galaxy Sample  (Abazajian et al. 2009 ) with r-band Petrosian magnitude limits 14 ≤ m r ≤ 17.
77 mag published by  Meert et al. (2015 , 2016, see Section 2. 3). This is a significant increase in the number of classified galaxies compared to similar available morphological catalogues (almost three times larger than the GZ2 and ∼ 50 times larger than the N10). The paper is organised as follows: in Section 2 we introduce the data sets used for training and testing our models, as well as the sample for which the catalogue described in this paper is released. In Section 3 we describe the Deep Learning model and its network architecture. In Section 4 and 5 we present the methodology and results of our models trained with the GZ2 and the N10 catalogues, respectively. Finally Section 6 details the content of our morphological catalogue and Section 7 summarises our main results.
Huertas-Company et al. 2011 catalogue In order to test how our T-Type classification compares with previous automated classifications, we use  Huertas-Company et al. (2011)  catalogue. This dataset contains an automated morphological classification in 4 types (E, S0, Sab, Scd) based on support vector machines of ∼ 670,000 galaxies from the  Meert et al. (2015)  SDSS DR7 sample. Each galaxy is assigned a probability of being in the four morphological classes instead of assigning a single class. We then transform these probabilities into T-Types by using equation 7 from  Meert et al. (2015).
In addition to having substantially improved photometry, stellar masses for the objects in this catalogue have recently been added  (Bernardi et al. 2017a). Therefore, further augmenting this rich data set with morphological information represents a significant added-value. The reader can refer to  Meert et al. (2015, M15 hereafter)  for a more detailed description of the sample selection. Once trained and tested, we apply our morphological classification models to all galaxies in that dataset. For each galaxy, we provide a probability for each of the questions listed in Table  1 , based on GZ2 catalogue. We use the N10 catalogue to derive a T-Type and also a probability value of being S0 versus E (to better separate galaxies with T-Type ≤ 0), plus an additional bar classification. In section 6 we summarise the catalogue content and give advise on how to properly use it.
DEEP LEARNING MORPHOLOGICAL CLASSIFICATION MODEL In this work, we apply Deep Learning algorithms using CNNs to morphologically classify galaxy images. Deep Learning is a methodology which automatically learns and extracts the most relevant features (or parameters) from raw data for a given classification problem through a set of nonlinear transformations. The main advantage of this methodology is that no pre-processing needs to be done: the input to the machine are the raw RGB cutouts for each galaxy. The main disadvantage is that, given the complexity of extracting and optimizing the features and weights in each layer, a large number of already classified images need to be provided to the machine. Fortunately, as explained in Section 2, there is a wealth of morphological catalogues in the literature overlapping our dataset, which we can use for training and testing our model performance.
Network architecture Given the high rate of success of previous works using CNNs for visual classification of galaxies  (Huertas-Company et al. 2015; Dieleman et al. 2015) , we adopt a similar (but not identical) CNN configuration. Testing the performance of different network architectures is beyond the scope of this paper, and we use the same input images and CNN configuration for each classification task. We use the KERAS library 2  , a high-level neural networks application programming interface, written in Python. The input to our machine are the RGB cutouts downloaded from the SDSS DR7 server 3  in j peg format, with 424×424 pixels of 0. 02×R 90 arcsec in size (per pixel, where R 90 is the Petrosian radius for each galaxy). The algorithm reads the images which are down-sampled into (69, 69, 3) matrices, with each number representing the flux in a given pixel at a given filter.
ACKNOWLEDGEMENTS The authors would like to thank the anonymous referee for comments that improved the paper. This work was funded by the  French National Research Agency (ANR)  project  ASTROBRAIN  (P. I. MHC) and by  UPenn  research funds (MB). The authors are also thankful to Google for the gift given to UCSC (" Deep-Learning for Galaxies ") which has also greatly contributed to making this work possible. Finally, HDS and MHC would like to thank our  UCSC  colleagues  Sandy Faber ,  David Koo ,  Joel Primack  for very productive discussions which helped to improve the content of the paper.
Acknowledgements. We are indebted to the anonymous referees for their valuable comments which greatly helped to improve the presentation of the paper. This research benefited from the support of the " FMJH Program Gaspard Monge in optimization and operation research " and from the support to this program from EDF. It also benefited from the  University Paris 13  BQR research project Connected and equicolored partitions of smartgrids. The problem of finding an st-bond arises in the context of partitioning a smart grid into areas having autonomy requirements. One of these requirements is to ensure the connectivity of each area for local energy transportation. Hence, partitioning a smart grid into two such areas reduces to finding an st-bond with additional properties.
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta (2016) framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a 'good' hierarchical clustering is one that minimizes some cost function. He showed that this cost function has certain desirable properties, such as in order to achieve optimal cost disconnected components must be separated first and that in 'structureless' graphs, i. e. , cliques, all clusterings achieve the same cost. We take an axiomatic approach to defining 'good' objective functions for both similarity and dissimilarity-based hierarchical clustering.
Introduction A hierarchical clustering is a recursive partitioning of a dataset into successively smaller clusters. The input is a weighted graph whose edge weights represent pairwise similarities or dissimilarities between datapoints. A hierarchical clustering is represented by a rooted tree where each leaf represents a datapoint and each internal node represents a cluster containing its descendant leaves. Computing a hierarchical clustering is a fundamental problem in data analysis; it is routinely used to analyze, classify, and pre-process large datasets. A hierarchical clustering provides useful information about data that can be used, e. g. , to divide a digital image into distinct regions of different granularities, to identify communities in social networks at various societal levels, or to determine the ancestral tree of life.
Developing robust and efficient algorithms for computing hierarchical clusterings is of importance in several research areas, such as machine learning, big-data analysis, and bioinformatics. Compared to flat partition-based clustering (the problem of dividing the dataset into k parts), hierarchical clustering has received significantly less attention from a theory perspective. Partitionbased clustering is typically framed as minimizing a well-defined objective such as k-means, kmedians, etc. and (approximation) algorithms to optimize these objectives have been a focus of study for at least two decades. On the other hand, hierarchical clustering has rather been studied at a more procedural level in terms of algorithms used in practice. Such algorithms can be broadly classified into two categories, agglomerative heuristics which build the candidate cluster tree bottom up, e.
A SELF-ADAPTIVE CONTROLLER FOR OPTIMIZING PV PRODUCTION This section first defines the general architecture of AmaSun, a self-adaptive multi-agent system which aims at maximizing energy production by controlling when to cool photovoltaic panels while using water reserves in an effective way, as only harvested rainwater can be used. The behaviours of the agents involved in this control system are then described. Figure  1  represents the global architecture of AmaSun and its environment. The AmaSun control system collects data from external modules such as the database in which the historical meteorological data of previous years are recorded, as well as the weather forecasts. It also collects data from local sensors through the DataManager module, including environmental data (temperature, wind power, solar irradiance) and internal data about the photovoltaic plant (panel temperatures, water level in the tank, current energy produced).
All these data are collected thanks to sensors at the level of the photovoltaic panels. The system is also connected to the cooling devices to which it can send activation commands. water when it is necessary. Because the goal of the Energy agent is to minimize the difference between EP and ROEP, we consider the criticality C of the Energy agent as this difference : C = ROEP -EP. The good behaviour of the system depends on the ability of the Energy agent to correctly estimate ROEP. If C is globally too low, the system will not perform enough cooling activations and the Energy agent will not be able to reach its goal. If C is globally too high, too many activations will be performed to try to decrease it, to the detriment of the other agents. These two problems occur when the ratio R used to convert the irradiance into ROEP is too high or too low.
Acknowledgment AM is thankful to  M. Lukac  for bringing this problem to her attention. AM and VA are thankful to  G. Kabatyanski  for pointing out the connection to the knapsack problem and to  M. S. Byrd  for many useful initial discussions. The authors are grateful to  A. Harrow  for his comments which have helped them to draw conclusions from preliminary results and to the unknown referee for much valuable feedback. AM and YZ acknowledge financial support during this work from the  Ministry of Education and Science (MES) of the Republic of Kazakhstan  via the contract #  339/76 -2015. AM also acknowledges financial support from the  Nazarbayev University ORAU  grant " Dissecting the Collective Dynamics of Arrays of Superconducting Circuits and Quantum Metamaterials " and  MES RK  state-targeted program  BR05236454.
In the study area, average doses of 2000 mm/year were applied since the end of the 1890s  [30] , leading to a strong accumulation in the surface horizon of urban organic matter and TM (mainly Zn, Pb, Cu and Cd [4]) as well as pharmaceutics  [31]. Soils were sandy textured in the surface Ap and eluvial E horizon, and a clayey-sand textured Bt horizon overlying either carbonated glauconite sand (Cs) or fragmented hard limestone (Cc). Selected characteristic soil data for Site 2, determined on bulk samples collected in several large soil pits, are presented in Table  2. For thin section preparation, a total of more than 30 undisturbed samples were also taken in all soil horizons. The Ap horizon contained up to ≈5% of organic matter, unusual for such sandy arable soils, but also large TM contents (Table  2 ) and secondary lime precipitates resulting from wastewater evaporation.
3%. Beam-focusing with a spot-size of 50 µm × 30 µm at the surface of samples was performed using a Xenocs FOX 2DMo 25-25P collimating multilayer optics. Runs were made at 21 mA and 55 kV, with dwell times ranging from about 100 to 1000 s per point, depending on TM concentrations and the analyzed surface. Samples were placed at 45 • of the X-ray beam and fluorescence spectra were collected at 90 • through an AXAS Vitus SDD (10 mm 2 of active surface) Ketek detector. An in-house program made in Visual Basic 6. 0 was used to synchronize the samples displacement (step size ∆x = 50 µm, ∆y = 30 µm) with motorized linear translation stages and the spectra acquisition recorded with GENIE-2000 program. Elemental distribution maps were generated using Origin©. Non-normality of element distributions was checked (Shapiro-Wilk) for all maps and Spearman correlation (ρ) was used to assess statistic correlation between pairs of elements.
Acknowledgments: The authors acknowledge financial support from the  French Ministry of Environment (MTES) , from  Conseil Régional de l'Île de France (CRIdF) ,  Conseil Général du Val d'Oise  ( CG95 ),  Agence de l'Eau Seine-Normandie (AESN)  ( AESN-031300 ) and  Syndicat Interdépartemental pour l'Assainissement de l'Agglomération Parisienne (SIAAP). The authors are also grateful to the three anonymous referees for their comments and helpful suggestions.
Another application of Gorgias was Market-Miner, an innovative agent for automated product pricing, mainly targeted for the retail sector  [47]. Market-Miner captures the points of view of different departments of a firm (production, financial, marketing, etc) together with information coming from internal (results of data mining on the corporate database) or external sources (e. g. , prices of the competition, weather forecast) and defines appropriate preferences in conflicting scenarios. Briefly, the options considered were at which price to sell a product: normal, high or low price. Scenario-based preferences captured various company policies, e. g. , a high pricing scenario when the object of the decision was a high technology product and an advertised invention, or a low pricing scenario when the product's type was within a category that the company wanted to hit the competition.