dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The field of functional genomics is producing a large amount of data, often represented as interaction networks -or undirected graphs. These graphs typically contain thousands of vertices, rendering the extraction of pertinent biological information a daunting task. Graph partitioning or clustering methods have been used to highlight groups of densely connected vertices  (Aittokallio and Schwikowski, 2006)  which, in the field of protein interactions, often correspond to clusters of proteins involved in the same cellular process(es). Cytoscape is a popular and versatile software platform  (Shannon et al. , 2003)  for network visualization and analysis. While a number of Cytoscape plugins such as ClusterMaker  (Morris et al. , 2011)  or ClusterOne  (Nepusz et al. , 2012)  can identify clusters from graphs, they mainly focus on visualizing the obtained clusters individually and independently as subnetworks to further investigate their node composition.
However, exploring the relationships between clusters in detail is as important as studying their internal composition. Indeed, while proteins involved in the same process(es) interact within clusters, links between clusters correspond to crosstalk between processes. Communication between processes can also be performed by proteins belonging to several clusters. Consequently, considering the links between network clusters permits a better understanding of the modularity of biological networks and the functional transitions imposed by the integrative organization levels, from proteins to functional modules to entire systems. To fill this gap, we have developed Clust&See, a truly interactive tool that can (i) automatically decompose a network into clusters; (ii) visualize those clusters as metanodes linked by several types of edges/relationships; (iii) manipulate the clusters for further detailed visualization, analyses and comparisons.
Importantly, results from external clustering tools can be analyzed with Clust&See. Currently, the R package "Linkcomm"  (Kalinka and Tomancak, 2011) , in which the LinkCommunities  (Ahn et al. , 2010)  and OCG  (Becker et al. , 2012)  algorithms are implemented, provides output files that are compatible with Clust&See (for further information on supported formats, see the online Documentation, http://tagc. univ-mrs. fr/tagc/index. php/software/clustnsee/ clustnseedocumentation). Finally, the modular structure of Clust&See makes it easy to implement other clustering algorithms directly in Java.
Acknowledgements We thank  Marine Veyssière  for the update of the PI3K interactome. This work is supported by the  "Agence Nationale de la Recherche " as a  Piribio grant  ( 09-PIRI-0028 ,  Moonlight  project to C. B) and as a partner of the ERASysbio+ initiative supported under the  EU ERANET Plus scheme in FP7  ( ModHeart Project ). B. R. is a PhD fellow of the  AXA Research Fund. C. C. is a postdoctoral fellow of the " Fondation pour la Recherche Médicale ".
Section 14 is provided to enable correlation function measurements with DR4 data and to aid theorists who wish to make mock DEEP2 catalogs from model galaxy data. It describes a set of files being distributed along with DR4, which describe the selection and redshift success probability for a DEEP2 target as a function of location on the sky. Finally, Section 15 summarizes the properties of the DEEP2 dataset via plots of numbers, colors, magnitudes, etc. , as a function of redshift. Depending on their interests, a reader may wish to focus on certain sections of this paper. For instance: • Users of DEEP2 Data Release 4 data: Key sections are §4 (survey parameters), §8 (assessment of biases in targeting), §10. 3 (presenting sample spectra), §11. 3 and §11. 4 (redshift results and completeness), §13 (describing the released data tables), §14 (describing the released 2-d selection functions) and §15 (describing trends in the sample with z).
4. Redshift ambiguities are rare and, if present, can largely be resolved in the future by using photometric redshifts  (Kirby et al. 2007 ). We therefore selected: • DEIMOS' highest-resolution grating, 1200 line mm -1 , typically covering 6500 Å to 9100 Å and yielding a spectral resolution R ≡ λ/∆λ ∼ 6000 with a 1 -wide slit. 30   Practical considerations influenced many of the final design decisions for DEEP2. The detailed layout of slitmasks on the sky was governed by the DEIMOS slitmask geometry (cf. §7). The final fields were chosen to have low Galactic reddening utilizing the  Schlegel, Finkbeiner, & Davis (1998)  dust map and to be well spaced in RA to permit good-weather observing at Keck. Field 1 (the Extended Groth Strip) was specially selected for its excellent multiwavelength supplemental data (as described in §4).
4, more than 8 billion years in the past); galaxy evolution over this time has on balance been a fairly regular and predictable process; and a good predictor of a galaxy's properties at any era is its stellar mass. An overarching hypothesis that may unite all of these findings is that the most important physical driver controlling a galaxy's evolution at z < 1. 4 is the growth of its dark halo mass versus time. On the other hand, several of the above findings point to close couplings between AGNs, starbursts, quenching, and galactic winds, and the causal connections among these seems more complex than the simple halo-driven model would predict. These issues are the great frontier for future work. The present data in DR4 do not exhaust the information from DEEP2. Many additional quantities have been derived from survey data, and have either been published separately (see Table  1 , or may be obtained by contacting the individual scientists involved.
SURVEY OVERVIEW AND FIELD SELECTION In Section 2, we presented the goals which were used to fix the basic properties of the DEEP2 survey. In the remainder of this paper, we describe the design and execution of the project and the resulting datasets in more detail. This first section serves as a basic introduction to the DEEP2 survey. To first order, DEEP2 is a magnitude-limited spectroscopic galaxy redshift survey with limiting magnitude of R AB = 24. 1. 31 Instrument and exposure parameters are summarized in Table  2. The survey was designed to be executed in four separate 0. 5 • (or 0. 25 • in the case of the Extended Groth Strip) × 2 • rectangular fields widely spaced in Right Ascension, with an average of 120 DEIMOS slitmasks per field. The spectral setup used the 1200-line/mm high-resolution DEIMOS grating with a spectral resolution of R ∼ 6000 and a central wavelength of 7800 Å; the typical exposure time was 1 hour per mask.
Masks were designed over 0. 5 • wide by 2 • long regions in Fields 2, 3, and 4, but not all of these masks were ultimately observed. As noted, Field 1 (EGS) is special: it hosts a wide variety of deep multiwavelength data from X-rays to radio. It is also one of the darkest and most dust-free regions of the sky. Since no z > 0. 7 preselection was done in Field 1, the surface density of targets on the sky there is roughly twice that in the other DEEP2 fields; in order to sample highredshift objects at the same number density as in other fields, only half as much area could be covered using the same number of slitmasks. As a result, the spectroscopy in EGS covers an area that is only ∼16 wide (but still 2 • long). All masks in EGS were observed. The field layouts and final slitmask coverage in Fields 2, 3, and 4 are shown in Figure  1.
Certain areas were redesigned following the first semester of survey operations, which caused a few regions to be observed twice with different masks (darker stripes). All DEEP2 masks, including these regions of double coverage, are included in DR4. The density of masks is especially high where CFHT 12K pointings overlap in Field 4. This duplication of coverage occurred because slitmasks were designed separately pointing by pointing in Fields 2-4 (but not EGS). Although photometry was obtained for three CFHT pointings in each of fields 2-4, due to limited time for spectroscopic observations field 23 (which had inferior photometry) and most of field 43 were not observed with DEIMOS. The photometric (pcat) catalogs for these fields are still included in the data release accompanying this paper.
The ongoing DEEP3 survey  (Cooper et al. 2012, in prep)  will increase the number of DEEP2 spectra by 50% and triple the weight of environmental data in the upper three fields of EGS. The Warm Spitzer Deep Extragalactic Survey (SEDS) will provide a total of 10 hours of integration time per pointing in IRAC Channels 1 and 2 (centered at 3. 6 and 4. 5 µm). The NEWFIRM survey will provide JK photometry to ∼24 AB mag, while the NEWFIRM Intermediate Band Survey and southern hemispheres. The recently-released deep SDSS photometry in this stripe should yield useful photometric redshifts down to DEEP2-like depths. The original design of the DEEP2 survey consisted of 480 slitmasks. The expected number of 135 slitlets per mask would then target ∼65,000 galaxies, of which 52,000 were expected to yield secure redshifts assuming an 80% success rate.
Details of the data and sources used for these comparisons are given in the footnotes to Table  6. Given the significant redshift failure rate in all distant galaxy surveys, it is important to have a consistent definition of "redshift success. " "Reliable", "secure", or "robust" redshifts are defined in this paper to be those with ≥95% probability of being correct, according to their authors (for all surveys described, this means quality codes 3 and 4 [or equivalent, e. g. including codes 13, 14, 23, and 24 for VVDS], but not 1 or 2. ) In comparing surveys we use the sizes of the presently published datasets for all surveys except zCOS-MOS, for which we use the design values. We now proceed to compare the power of the various z ∼ 1 surveys using a variety of metrics. Numerical results are summarize in Table  6.
The volume used is computed from the areas in Table  6 , bounded by the redshifts corresponding to the 2. 5 percentile and 97. 5 percentile point within a given survey (or by z = 0. 2 and z = 1. 2, the redshift range the team uses for science, in the case of PRIMUS). For surveys other than DEEP2 and TKRS (where the actual percentiles are used), these redshift limits are computed from analytic redshift distributions of the form n(z) ∝ z 2 e -z0 , using fits to z 0 as a function of limiting magnitude determined as described in  Coil et al. (2004b) , but using the full DEEP2 dataset for calibration. This results in values of z 0 of 0. 26, for DEEP2 and TKRS, 0. 224 for VVDS-wide and zCOSMOSbright, and 0. 283 for VVDS-deep. Among the contemporaneous distant spectroscopic surveys (i. e. , excluding PRIMUS), DEEP2 has the best combination of total number and volume sampled, with 2.
SELECTION EFFECTS IN THE FINAL TARGET SAMPLE The final DEEP2 target sample suffers from a variety of known selection effects. These effects should in general be taken into account and/or corrected for when utilizing the DEEP2 dataset. A variety of methods for doing so have been implemented in DEEP2 science papers; see §3 and Table  1  for examples. • Galaxy color bias due to the R magnitude limit Selection according to a fixed apparent R-band magnitude limit causes the resulting sample to reach different depths (in terms of absolute magnitude) for galaxies of different intrinsic colors at the same redshift. At z ∼ 0. 5, the R-band filter corresponds closely to restframe B, so objects are selected down to the same B-band absolute magnitude independent of color. Below that redshift, R-band selection favors redder galaxies, while above this, it favors bluer galaxies.
As shown by Figure  13 , this region of the color-color plot is populated by intrinsically red galaxies beyond z ∼ 0. 7. Loss of such galaxies was estimated by  Willmer et al. (2006)  using the higher-resolution HST ACS images in EGS and partial redshift data. We redo that calculation using final EGS redshifts and a larger database. Figure  18  plots the color-color diagram for EGS but restricts to objects in the ACS mosaic fainter than R = 23. 0, where misclassification is most likely (cf. Figure  8 ). DEEP2 stellar candidates (p gal < 0. 2) that are confirmed as stars in EGS ACS images are plotted in blue; those shown instead to be galaxies are red. The latter are missed by DEEP2 and are thus a concern. Galaxies are lost in a narrow band in B -R, which corresponds to certain combinations of intrinsic redsequence color and redshift (cf.
Other redshift quality codes which the user may assign indicate that an object's spectrum is completely masked by instrumental artifacts so that the object was effectively never observed (Q = -2), is a star (Q = -1), yields no useful redshift information (Q = 1), or may potentially yield redshift information but needs more analysis or re-reduction (Q = 2); many, but not all, Q = 2 cases may be resolved with more inspection. Section 13 below for a fuller description of the DEEP2 redshift quality codes. During the zspec process, the user may also fill in one or more comment codes that signify additional information about the data; the standard codes are also described in §13. These codes are useful, but their application has varied among various zspec users, and they are the least homogeneous aspect of the DEEP2 database.
By the summer of 2007 (the time of the review), most masks observed in Field 1 had gone through multiple zspec checks. The zspec results from all reviewers were combined together into a database with 14,509 independent entries, where an entry corresponded to a single observation of a single target; i. e, multiple observations (on different slitmasks or at very different times) of the same target were treated as separate entries in the database. Out of all entries in the database, 10,763 entries (75%) had gone through the zspec process more than once. Several criteria were applied to flag cases of possibly erroneous redshifts or redshift quality among the multiply-checked targets in the Extended Groth Strip. The selected objects (1102 in total) were: A) Targets with redshift quality Q > = 3 in all reviews, but with differences in assigned redshift greater than 0.
Data are given for targeted objects only; many serendipitous objects are visible on the 2-d spectra, but they have been uniformly analyzed. Their presence is signaled by the comment code offser ("offset serendip") in the comment field of the targeted galaxy (see below). Table  10  is similar to the DEEP2 zcat table at http://deep. berkeley. edu/DR4/, but certain confusing or rarely-used quantities have been omitted (we describe those quantities in §13. 0. 1), while some other quantities derived from mask design files have been added (the dropped quantities continue to be available in the zcat available online. ) Table  10  is thus a melding of data in the DEEP2 DR4 spectral database, the previously-released pcat photometry catalogs  (Coil et al. 2004b)   59  , and data from the slitmask design files, collected together for easy reference.
TRENDS WITH REDSHIFT We conclude this overview of the DEEP2 survey by illustrating certain major trends in the data as a function of redshift, summarizing properties of the DEEP2 dataset. Figure  48  shows redshift histograms for each of the four fields, using only reliable (Q = 3 and Q = 4) redshifts. These figures illustrate graphically that, fractionally, the contribution of Q = 3 (> 95% reliable) redshifts is rather small; the great majority (83%) of secure redshifts have Q = 4 (> 99% reliable). Field 1 (EGS) is well sampled at all redshifts, whereas our BRI color pre-selection has efficiently eliminated foreground galaxies in Fields 2, 3, and 4. As expected, strong peaks due to large-scale structure are evident, which differ in detail from field to field, demonstrating the need for statistically independent samples to beat down cosmic variance.
CONCLUSIONS AND ACKNOWLEDGEMENTS This paper has presented DEEP2 Data Release 4, the first spectroscopic database resulting from a complete DEEP2 Galaxy Redshift Survey sample, containing 52,989 spectra and 38,348 reliable redshift measurements for objects as faint as R AB = 24. 1. The information in this paper is intended as a handbook for users of the survey, as well as for those using the DEEP2 data reduction pipelines to reduce DEIMOS data. Both raw and reduced spectra, photometry, and all redshifts are available for download from the Data Release 4 website (http://deep. berkeley. edu/DR4/) as well as other websites listed in §10. We do not expect this to be the final data release from the DEEP2 survey. Future releases may include improved photometry, revised redshift estimates (e. g. from incorporating photometric redshift information for Q = 2 objects), improved data reductions and extractions, and/or additional derived parameter measurements not included here.
Computing hardware used to analyze DEEP2 data was provided by Sun Microsystems. The HST ACS imaging mosaic in EGS was constructed by Anton Koekemoer and Jennifer Lotz and was funded by grant HST-AR-01947 from NASA. NASA imaging of the original Groth Strip was planned and executed by Ed Groth and Jason Rhodes with support from NAS5-1661 and NAG5-6279 to the WFPC1 IDT. Support for this work was provided by NASA through Hubble Fellowship grants 51256. 01 and 51269. 01 awarded to ENK and MCC, respectively, by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc. , for NASA, under contract NAS 5-26555. Sandra Faber would like to thank CARA for a generous research grant and the Miller Institute at UC Berkeley for a Visiting Miller Professorship, during which much of this paper was written.
With the advent of new Multi-Electrode Arrays techniques (MEA), the simultaneous recording of the activity up to hundreds of neurons over a dense configuration supplies today a critical database to unravel the role of specific neural assemblies. Thus, the analysis of spike trains obtained from in vivo or in vitro experimental data requires suitable statistical models and computational tools. The EnaS software [7], developed by our team, offers new computational methods of spike train statistics, based on Gibbs distributions (in its more general sense, including, but not limited, to the Maximal Entropy -MaxEnt) and taking into account time constraints in neural networks (such as memory effects). It also offers several statistical model choices, some of these models already used in the community (such GLM  [6]  and the conditional intensity models  [5] ), and some others developed by us (  [1]  and  [2] ), and allows a quantitative comparison between these models.
It also offers a control of finite-size sampling effects inherent to empirical statistics. EnaS allows large scale simulation thanks to our recent study  [2]  (hundreds of neurons) with spatio-temporal constraints. It's available as a Graphical User Interface in order to make the tools more accessible by non-programmers. Within EnaS framework, programmers are also allowed to implement new tools and integrate them with the existing modules. We featured EnaS with parallel processing on personal computers (using MPI) and on clusters (Using OpenMP).
This UDKE provide a conceptual model of a maintenance system that combines knowledge, user experience and Augmented Reality techniques. The context awareness notion was absent from this project so the possible decisions and information provided to the user were not relied to his task and did not consider his feedback. Kwon  [26]  [27] has developed a framework called ubiDSS. ubiDSS is a framework of multi-agent and context-aware based proactive decision support system. The architecture of this framework is composed by a number of subsystems: context subsystem, dialogue subsystem, knowledge subsystem, model management subsystem, and a database management subsystem. Even that the application field of the ubiDSS system is far from industry, it was very important to study the interactions between the components of its framework and the models behind.
This paper presents an overview of the ImageCLEF 2013 lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the cross-language annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and botanic collections. Over the years, by providing new data collections and challenging tasks to the community of interest, the Im-ageCLEF lab has achieved an unique position in the multi lingual image annotation and retrieval research landscape. The 2013 edition consisted of three tasks: the photo annotation and retrieval task, the plant identification task and the robot vision task. Furthermore, the medical annotation task, that traditionally has been under the ImageCLEF umbrella and that this year celebrates its tenth anniversary, has been organized in conjunction with AMIA for the first time.
Introduction Since its first edition in 2003, the ImageCLEF lab initiative has focused on providing an evaluation forum for the cross-language annotation and retrieval of images  [1]. The main motivation behind ImagCLEF is the need to support multilingual users from a global community accessing the ever growing body of visual information. Thus, the main goal of ImageCLEF is to support the advancement of the field of visual media analysis, indexing, classification, and retrieval, by developing the necessary infrastructure for the evaluation of visual Fig. 1. Number of registered groups versus number of groups that submitted at least one valid run since 2009. In 2012 and 2013, we report also the total number of groups that initiated the registration process but that, for several reasons, were not able to complete it in time.
information retrieval systems operating in monolingual, language-independent and multi-modal contexts, providing reusable resources for such benchmarking purposes. To meet these objectives, ImageCLEF organises tasks that benchmark the annotation and retrieval of diverse images such as general photographic and medical images, as well as domain-specific tasks such as plant identification and robot vision. These evaluation tasks aim to support and promote research that addresses key challenges in the field including: 1) visual image annotation with concepts at various levels of abstraction that relies not only on manual, and thus reliable, training data but also on automatically acquired and thus noisy, labelled samples, 2) scientific multimedia data management through the particular case of botanical data identification, and 3) the shift in the area of robot vision from visual place recognition to multimodal place recognition.
Many research groups have participated over the years in its evaluation campaigns and even more have acquired its datasets for experimentation. Figure  1  shows the number of registered groups, and of groups that eventually submitted a run, since 2008. In 2013, over 200 research groups regis-tered, with 42 of those submitting runs officially to the ImageCLEF tasks. The impact of ImageCLEF can also be seen by its significant scholarly impact indicated by the substantial numbers of its publications and their received citations  [2]. The rest of the paper is organized as follows: section 2 describes the three subtasks of the 2013 edition: the photo annotation and retrieval task (section 2. 1), the plant identification task (section 2. 2), and the robot vision task (section 2. 3). Section 2. 4 describes the AMIA associated medical tasks.