dataesr/scientific-paragraphs-categorization huggingface dataset:
Regions of interest (ROI) analysis of the PET/CT images were used to generate time activity curves corresponding to: 1) the arterial pool in the descending aorta; and 2) areas of tissue within the liver that corresponded to the tumor and adjacent liver samples profiled by expression arrays. PET kinetic analysis was then applied based on a 2-tissue compartment (2TC) model of 18 F-fluorocholine pharmacokinetics in liver tumor and liver tissue  [34, 35]. Pharmacokinetic parameters K 1 ; k 2 ; k 3 ; k 4 ; K 1 =k 2 , and Flux for each 2TC model corresponding to each sample were estimated using PMOD 3. 4 (PMOD Technologies, Zurich Switzerland) and assembled to form a 6 Â 50 Pet kinetics data matrix for the 50 tissue samples included in the experiment. Tumor and adjacent non-tumor liver tissue specimens were obtained subsequently during surgery, and RNA was extracted from homogenized frozen tissue lysates in RLT Plus buffer with the AllPrep DNA/RNA Mini kit (Qiagen, Valencia, CA) following manufacturer's protocol.
The isolated RNA was stored at -80 0 0 until used. The quality of the total RNAs was checked on a Bioanalyzer using RNA 6000 Nano chips (Agilent, Santa Clara, CA). The RNA samples were processed following the WG-DASL assay protocol (Illumina Inc. , Sunnyvale, California) and the resulting PCR products were hybridized onto the Illumina HumanHT-12 v4 Expression BeadChips included over 24,000 transcripts with genome-wide coverage of well-characterized genes, gene candidates, and splice variants. Arrays were scanned using the iScan TM instrument and expression levels were quantified using GenomeStudio software (Illumina Inc. , Sunnyvale, CA). Gene-level expression values were assembled to form a 20792 × 50 data matrix where the rows represented 20792 genes and columns represented 50 adjacentnormal and tumor samples obtained from 20 patients.
Acknowledgments We thank the staff of the  Pathology Shared Resource (PSR)  and  Genomic Shared Resource (GSR) of the University of Hawaii Cancer Center  for their support of tissue and genomic data collection for this study. The PSR and GSR are supported in part by  NCI   P30 CA071789. We also thank the staff of The  Hamamatsu/Queen's PET Imaging Center (PIC)  for supporting the image acquisition and analysis for this study. The PIC is supported  NCI   R01 CA161209-04.
Building upon the fact that the nodes belonging to the maximal k-core of the graph have good spreading properties  15  , here we further refine this set of the most influential nodes, showing that the nodes belonging to set T defined above perform even better, leading to faster and wider epidemic spreading. We study real-world networks arising from online social networking and communication platforms (all datasets are publicly available  29  ). In particular, we investigate the following network datasets: (i) EMAIL-ENRON and (ii) EMAIL-EUALL, two email communication networks; (iii) EPINIONS which is an online social network created from the product review website Epinions. com; (iv) WIKI-VOTE, a network created by all the voting data between administrators of Wikipedia; (v) WIKI-TALK, created by the interaction data between Wikipedia users; (vi) SLASHDOT, which is created by the friendship relationships in the technology review website Slashdot.
org. All datasets are considered undirected and unweighted; also, the largest connected component was used in the experiments. High level characteristics of the networks are shown in Table  1  (see Supplementary Note 1 for more details about the datasets). Before presenting the results about the spreading properties, we examine the maximum level of the K-truss decomposition, i. e. , value K max , for the various graphs. As we can observe from Table  1 , K max values vary from dataset to dataset, but compared to the k max values of the k-core decomposition, they tend to be much smaller. This is rather expected since the K-truss decomposition relies on triangle participation, which is a more strict criterion compared to node degree. This last point is also a justification for the differences on the number of nodes belonging to the truss set T and core set C (i.
e. , ≥ ≥… ≥ OPT OPT O PT V 1 2. In order to examine how the nodes detected by the K-truss decomposition are distributed among the most efficient (optimal) spreaders, we consider a variable size window W over the ranked nodes and define T P W to be the fraction of nodes of set T that can be found within W as follows: Table  2. Evaluation of the spreading performance per step of the process. Average number of infected nodes per step of the SIR model for the proposed truss method and the two baselines core and top degree, using β close to the epidemic threshold of each graph and γ = 0. 8. At the Final step column, we show the total number of infected nodes at the end of the process (Max step), with standard deviation σ. Observe that, in almost all datasets the truss method achieves higher spreading especially during the first steps of the process.
Also, as the Max step column indicates, the epidemic dies out at an earlier time step when triggered by a node of the maximal K-truss subgraph. = / / , ( ) T T P T W V 3 W W where T W is the set of nodes ∈ T v that are located in the window W of size W (in a similar way, we can define C P W for the nodes of the maximal k-core subgraph). We are interested in examining how the quantities T P W and C P W behave with respect to the size of the window W. Figure  3  depicts the distribution of the top-truss T P W and top-core C P W nodes, for various sizes of window W (i. e. , fractions of the most efficient spreaders). As we can observe, for almost all datasets, T P W reaches the maximum value (i. e. , 100%) relatively early and for small window sizes, compared to C P W. The maximum value of T P W indicates that we have found all the nodes belonging to set T in the window of fractional size W.
A slightly different behavior is observed in the WIKI-TALK and SLASHDOT graphs; in both graphs, the values of T P W and C P W are very close to each other for almost all choices of window W, indicating that both sets have almost the same overlap with the set of optimal spreaders. Nevertheless, as we have already presented in Table  2 , for those two datasets the spreading performance of the truss nodes achieved during the first steps of the epidemic is much better. Furthermore, we are interested to study the distribution of the nodes' truss number t node with respect to window W. Similar to what described above, we consider a fraction of the best spreaders in the graph (as specified by W) and we examine the distribution of all truss numbers (and not only the maximum one) within it. Since nodes with high truss number are of particular importance here, we have considered groups of nodes as follows: (i) individual groups for each of the top five truss numbers, i.
e. , K max to -K max 4. That way, the first group contains nodes with truss number equal to K max , the second group of nodes with truss number -K max 1 and so on. (ii) The rest of the groups concern truss numbers in the range -K max 5 to = K 2, grouping together five consecutive truss numbers each time. For example, the sixth group contains nodes with truss number in the range -K max 5 to -K max 9. Note that, the last group may contain less than five truss numbers. Figure  4  depicts the distribution of truss numbers for various values of window W. The colors on each bar correspond to the groups of truss number (darker colors for truss numbers closer to the maximum one). As we can observe in most of the datasets, for small values of window W, a large number of the nodes belong to the first group, i.
The naive solution of choosing multiple nodes from the maximal K-truss subgraph will not perform well, since those nodes are clustered together in the graph and share many common neighborhood nodes. Thus, as suggested in the related literature  15  , a good strategy is to also consider the distance between them, as expressed by the number of hops needed to reach each other. So far we have studied the effect of our method in real datasets by simulating spreading cascades. It is of great interest to also consider the identification of influential spreaders by following real information flow in social networks, as has been suggested by Pei et al. 37. Unfortunately, a lot of difficulties arise in such a case considering the lack of ground truth information that can actually represent the diffusion of a specific idea as is simulated by epidemic models.
Additionally, the problem of how to consider a time frame to analyze the influence of every node of the network arises, which can alter the results depending on the setting chosen. Finally, in real information flow, some nodes are not found performing any activity, making the comparison of the methods even harder. We have tested our method at the Facebook dataset  38  and the nodes found after performing K-truss decomposition tend to be more effective in terms of spreading compared to those located by the k-core decomposition.
Acknowledgements F. D. M. is a recipient of the  Google Europe Fellowship in Graph Mining , and this research is supported in part by this Google Fellowship. M. -E. G. R. is funded by a  DigiCosme Ph. D. Fellowship. M. -E. G. R. is funded by a  DigiCosme Ph. D. Fellowship  and this research is supported in part by  Labex DigiCosme.
The advent of the Atacama Millimeter/submillimeter Array (ALMA) is opening up a new window for the study of the faint DSFG population. Its significantly higher angular resolution compared to single-dish telescopes (< 3 ′′ ), and the unparalleled sensitivity allow us to reach flux density levels in (sub)millimeter continuum maps even deeper than those achieved by studies of galaxy cluster fields or based on stacking analysis. Several recent studies have individually pinpointed (sub)millimeter sources down to 0. 1 mJy in the 1-mm band  (Hatsukade et al. 2013; Ono et al. 2014; Carniani et al. 2015; Oteo et al. 2015; Hatsukade et al. 2016; Dunlop et al. 2016). Some of these surveys have used clever approaches by taking advantage of archival data  (Ono et al. 2014; Carniani et al. 2015; Fujimoto et al.
2016) , including ALMA calibration fields  (Oteo et al. 2015). Recently,  Fujimoto et al. (2016)  were able to reach down to a flux limit of 15µJy at 1. 2mm, providing the deepest measurements of the number counts to date, and allowing them to resolve most of the CIB into individual sources. Despite the substantial progress, the current studies are still affected significantly by cosmic variance and are not "blank-field" in nature (as some of them target overdense fields). Most importantly, the lack of sufficiently deep complementary data have only permitted the characterisation of a handful of sources  (Hatsukade et al. 2015; Fujimoto et al. 2016; Yamaguchi et al. 2016). Using ALMA in Cycle 2, we have conducted a deep ALMA Spectroscopic Survey (ASPECS) of a region of the Hubble Ultra Deep Field (UDF), covering the full 3-mm and 1-mm bands.
ALMA observations and data reduction The ASPECS survey setup and data reduction steps are described in detail in Paper I  (Walter et al. 2016 ). Here we repeat the most relevant information for the study presented here. ALMA band 3 and band 6 observations were obtained during Cycle-2 as part of projects 2013. 1. 00146. S (PI: F. Walter) and 2013. 1. 00718. S (PI: M. Aravena). Observations in band-3 were conducted between July 01, 2014 to January 05, 2015, and observations in band 6 were conducted between December 12, 2014 to April 21, 2015 under good weather conditions. Observations in band 3 were performed in a single pointing in spectral scan mode, using 5 frequency tunings to cover 84. 2 -114. 9 GHz. Over this frequency range the ALMA half power beam width (HPBW), which corresponds to a primary beam (PB) response of 0.
Calibration and imaging was done using the Common Astronomy Software Application package (CASA). The calibrated visibilities were inverted using the CASA task CLEAN using natural weighting. To obtain continuum maps, we collapsed along the frequency axis in the uv-plane and inverted the visibilities using the CASA task CLEAN using natural weighting and mosaic mode. We use the Multi-frequency Imaging Synthesis (MFS) algorithm with nterms=1 as the joint implementation of nterms>1 and mosaic mode are not yet available in CASA. This implies assuming a first order polymial fit for point sources along the frequency axis, which is the best assumption for low signal to noise data (most sources with S/N< 10) as in this paper (see CASA cookbook and  Rau & Cornwell 2011). We also tested the effect of using different frequency weightings in the visibility plane, however no significant changes were seen in the final collapsed images.
We here also include the ALMA 1. 2-mm data flux densities, however we note that the optical/infrared data has a much stronger weight given the tighter constrains in this part of the spectra. We do not include Herschel photometry in the fits since its angular resolution is very poor, being almost the size of our target field for some of the IR bands. The Herschel photometry is thus heavily blended. For each individual galaxy, we perform SED fits to the photometry fixed at the best available redshift. MAGPHYS delivers estimates for the stellar masses, star formation rate (SFR), dust mass and IR luminosity. Even though for most galaxies we do not have photometric constraints on the observed IR SED, MAGPHYS employs a physically-Figure  7. Distribution of stellar masses and SFRs (obtained from SED fitting) for the galaxies detected in our ALMA 1.
The effect of cosmic variance A number of recent studies have used the archival ALMA 1. 2-mm data to provide constraints on the EBL at 1. 2-mm. These studies measure significantly higher integrated intensities at 1. 2-mm compared to our estimates  : Fujimoto et al. (2016)  measure the number counts down to a flux limit of 15 µJy, just below our ALMA UDF flux limit, with an integrated intensity of ∼ 22 Jy deg - 2  ;  Hatsukade et al. (2013)  integrated their number counts down to 0. 15 mJy, obtaining an intensity of ∼ 16. 9 Jy deg -2 (converting their measurement from 1. 3-mm to 1. 2-mm);  Ono et al. (2014)  measures ∼ 11 Jy deg -2 down to 0. 1 mJy; similarly,  Carniani et al. (2015)  measures ∼ 17 Jy deg -2 down to 0. 1 mJy at 1. 2-mm. To derive the fraction of the EBL at 1. 2-mm resolved, most of these literature results use early measurements from the Far Infrared Absolute Spectrophotometer (FIRAS) on board of the COBE satellite  (Fixsen et al.
We thank the anonymous referee for her/his positive feedback and useful comments. M. A. acknowledges partial support from  FONDECYT  through grant  1140099. FW, IRS, and RJI acknowledge support through  ERC grants COSMIC-DAWN , DUSTYGAL, and COSMICISM, respectively. FEB and LI acknowledge Conicyt grants  Basal-CATA PFB-06/2007  and Anilo  ACT1417. FEB also acknowledge support from  FONDECYT Regular 1141218 (FEB) , and the  Ministry of Economy, Development, and Tourism's Millennium Science Initiative  through grant  IC120009 , awarded to  The Millennium Institute of Astrophysics, MAS. EdC gratefully acknowledges the  Australian Research Council  as the recipient of a  Future Fellowship  (project  FT150100079 ). DR acknowledges support from the  National Science Foundation  under grant number  AST-1614213  to  Cornell University.
IRS also acknowledges support from  STFC  ( ST/L00075X/1 ) and a  Royal Society / Wolfson Merit  award. Support for RD and BM was provided by the  DFG priority program 1573  'The physics of the interstellar medium'. AK and FB acknowledge support by the  Collaborative Research Council 956 , sub-project  A1 , funded by the  Deutsche Forschungsgemeinschaft (DFG). PI acknowledges Conict grants  Basal-CATA PFB-06/2007  and Anilo  ACT1417. RJA was supported by  FONDECYT  grant number  1151408. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2013. 1. 00146. S and ADS/JAO. ALMA# 2013. 1. 00718. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.
This paper examines the eco-innovation dynamics in the chemical sector by analyzing Sustainable Chemistry (SC) technologies and the organizations that generated those technologies. First, we build an original dataset of patents and investigate trends emerging from patent statistics. Second, by using a clustering algorithm for the analysis of citation networks, we identify the main clusters of innovations that are driving the evolution of the field and analyze the demography of involved organizations. We found that SC is emerging slowly, and remains importantly concentrated in the US and in Europe. Public organizations, in particular US organizations, have played an important role in the development of the field. Relatively established and large chemical companies are the most active organizations and their continuity is more evident in the less recent clusters.
2004 ). However, by exploiting economies of scale and scope, large and established chemical companies have showed great continuity in their production and innovative activity  (Oltra and Saint-Jean 2007). In order to answer the mentioned questions, firstly we reviewed the specialized literature related to SC and interviewed a number of experts. On this basis, we identified SC technologies and built an original dataset of USPTO patents granted between 1971 and 2011 for those technologies. One of the reasons behind the lack of quantitative studies relating to SC relies in the difficulties posed by the selection of the relevant dataset of patents. Those difficulties are due to both the high heterogeneity of the chemical sector and the nature of emerging field of SC. Therefore, we put particular emphasis on the construction and validation of the patent dataset.
The dataset has been created by combining keyword search with technological class search, and has been defined with the assistance of experts in the field. Secondly, we analyzed the main trends emerging from patent statistics, highlighting how SC evolved over time and spread among different technological areas, countries and organizations. Finally, we constructed a network of citations among SC patents and applied to it a clustering algorithm. That allowed us mapping the most important clusters of innovations of SC, and thus discussing both the areas where organizations are concentrating their research efforts and the characteristics of such organizations. The rest of the paper is organized as follow. Section 2 is an overview of the chemical sector and of the main SC technologies. Section 3 illustrates the data and methods.
Data and methods One of the reasons behind the lack of quantitative studies relating to eco-innovation in the chemical sector relies in the difficulties posed by the selection of the relevant dataset of patents. Those difficulties are due to both the high heterogeneity of the chemical sector and the nature of emerging field of SC. Indeed, being SC an emerging field, it is still not clear what are the most important technologies, in the community of practitioners there are different visions on "how to do things" and a variety of alternatives are explored. In this paper, we try to overcome such difficulties by putting particular emphasis on selecting and validating the patent search strategy. Since SC spreads among different technological classes and there is not a single set of technological classes that clearly refers to SC, the dataset has been created by combining keyword search and technological class search.
More in particular, we first selected a list of keywords for each of the SC technologies detailed in section 2, and included a number of generic terms as well (e. g. , alternative feedstocks, biodegradable products etc. ). In writing up such list, we also relied on previous patent research filters, namely Nameroff et al. (  2004 ) and OECD (2011). We then discussed the keyword list with chemists working in different chemical fields, asking them to integrate or modify the keywords list. We searched for the final keyword list on titles, abstracts and claims of patents granted from 1971 to 2011 by the USPTO. In order to minimize the number of irrelevant patents contained in our dataset, the keyword search has been limited to patents belonging to the chemical field 6. Following OECD (2011), we added as well a number of technological sub-classes that can be clearly associated with SC.
Finally, a sample of the dataset has been analyzed by an expert and the dataset has been further refined. We obtained a final dataset of 27355 patents, which, we believe, represent a good, although not perfect, indicator of the technological knowledge relating to sustainability research in chemistry. To analyze SC technological knowledge, we used a clustering algorithm for the analysis of citation networks. Analysis is performed on backward citation data associated with the 27355 patents contained in our dataset. In particular, we created a network of citations among SC patents, so that SC patents correspond to the vertices of the network and are connected with each other by a number of arcs, which symbolize citational links among patents. Each patent represents a discrete piece of technological knowledge that has passed the scrutiny of trained specialists and has been granted on the basis of relatively objective standards.
Since it is a legal duty for the assignee of a patent to disclose the existing knowledge, each cited patent represents a previously existing piece of knowledge that has been incorporated and further developed by the citing patent. Citations among patents, by making explicit the epistemic links among the pieces of knowledge from which a field emerges and grows, can be used to trace the dynamics of technological knowledge underlying the field  (Mina et al. 2007). To that purpose, we applied the Island algorithm, which is implemented by the software Pajek, to our SC network. The algorithm enables us to identify and explore the main clusters of innovations that populate the domain of SC, thus providing an indication of the technologies where organizations are concentrating their research efforts.
As demonstrated in  Batagelj et al. (2006) , each sub-network identified by the Island algorithm has the same topic; therefore Islands can be considered as thematic clusters and can be here used to identify the main clusters of innovations that contributed to the development of SC. Moreover, we applied to each cluster a number of algorithms that allow visualizing the time evolution of the cluster and the relative importance of vertices (as revealed by traversal weights). This will permit 7 Entry vertices are those vertices that are not cited within the data set, while exit vertices are vertices that are not citing within the data set. Traversal weights on arcs are calculated in the following way. In an acyclic network there is at least one entry vertex and at least one exit vertex. Let us denote with I and O the set of all entries and all exits, respectively.
We used such information firstly to assess trends relating to the evolution and distribution of SC innovative activity, and secondly to identify the main clusters of innovations and organizations involved in the development of SC. On this basis, we will try to detect whether or not there has been a change in the characteristics of involved organizations. This methodological approach has of course limitations. The first set of limitations relate to the patent dataset. Despite our efforts to validate the patent search strategy, our dataset may contain a number of irrelevant patents (patents that do not relate to SC) and may have missed some relevant patents. To the best of our knowledge, there is not a way to completely avoid this type of errors. However, the incorporation of practitioners in construct measurement, as has been done in this study, may mitigate the importance of such errors  (Nelson et al.