dataesr/scientific-paragraphs-categorization huggingface dataset:
The user can optionally specify a startTime and endTime (in the same range of formats as date) and then the tool will only count observations on or after startTime and/or before endTime. If the end time is only a date with no time specified, it is assumed to be inclusive; that is, the end time is the end of the specified day. Hence, using the options of startTime ''01/01/08'' and endTime ''31/01/08'' would only count observations in January 2008. Any data are binned into each grid cell and no interpolation of the data is performed. The ''time'' value in the netCDF is set as follows: If any data contain date/time information, then the time is set to the midpoint of the Convert in situ data to a netCDF file range of valid times (i. e. , times corresponding to data that have been counted). Otherwise, if startTime and end-Time are both set, time is set to the midpoint of these.
Acknowledgments. This work was funded by the  European Space Agency (ESA) Support to Science Element (STSE)  through the  OceanFlux Greenhouse Gases project  (Contract  4000104762/11/I-AM ) and the  U. K. NERC Carbon and Nutrient Dynamics and Fluxes over Shelf Systems (CANDYFLOSS)  project (Contract  NE/K002058/1 ). The  Surface Ocean CO 2 Atlas (SOCAT)  is an international effort, supported by the  International Ocean Carbon Coordination Project (IOCCP) , the  Surface Ocean Lower Atmosphere Study (SOLAS) , and the  Integrated Marine Biogeochemistry and Ecosystem Research program  (IMBER), to deliver a uniformly quality-controlled surface ocean CO 2 database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.
To avoid residual movement of immersion oil due to changing the objective position during the z-scan, we introduced a 1-s lag time between changing the focal height and exposure. The exposure time was 100 ms per image. To segment the biofilms into individual cells from the 3D images, custom code was written in Matlab. Briefly, images were treated separately for each z-plane, bandpass filtered, thresholded, and after that, binary objects were connected between the different z-planes to obtain 3D volumes for each cell inside the biofilm. By manually verifying the segmentation of 10 biofilms of ∼200 and ∼500 cells, we determined that the algorithm correctly segments 95% of the cells, whereas the remaining 5% are clusters of >2 cells, of which 63% are clusters of two cells.
Ice is a highly transparent material in the visible. According to the most widely used database (IA2008;  Warren and Brandt, 2008) , the ice absorption coefficient reaches values lower than 10 -3 m -1 around 400 nm. These values were obtained from a vertical profile of spectral radiance measured in a single snow layer at Dome C in Antarctica. We reproduced this experiment using an optical fiber inserted in the snow to record 56 profiles from which 70 homogeneous layers were identified. Applying the same estimation method on every layer yields 70 ice absorption spectra. They present a significant variability but absorption coefficients are overall larger than IA2008 by 1 order of magnitude at 400-450 nm. We devised another estimation method based on Bayesian inference that treats all the profiles simultaneously.
, 2006; Warren and Brandt, 2008). In general the trend has been towards lower absorption values, and the most recent and widely used compilation by  Warren and Brandt (2008)  suggests values as low as 6 × 10 -4 m -1 at the frontier between the UV and the visible (this ice absorption dataset is called IA2008 hereinafter). This corresponds to light intensity decreasing by approximately a factor of 2 per kilometer in pure ice. A related debate concerns the position of minimum absorption, which in general has shifted towards shorter wavelengths with successive updates. Estimation of the absorption coefficient from light transmission measurements directly in ice slabs proved to be difficult for several reasons. First, to measure significant decrease due to the absorption, the path length in the slab needs to be large compared to the inverse of the absorption coefficient, i.
(2006). To reproduce the experiment and permit a statistical analysis, we collected 56 profiles. Moreover, we devised a Bayesian estimation method to retrieve the absorption from this large dataset. This provides not only the most likely ice absorption spectrum but also the uncertainty related to the estimation process and observation errors. Second, it is possible that the insertion of the optical fiber in the snow disturbs the radiance field near the fiber tips where the light ultimately enters the spectrometer  (Light et al. , 2008; Petrich et al. , 2012). Since the fiber housing is likely to have a lower reflectance than snow, the housing absorbs more than the surrounding snow. Intuitively, this could increase the apparent AFEC and in turn result in an overestimation of the absorption.
Nevertheless, for the sake of simplicity, we use the term "profile of log-radiance" throughout this article. SOLEXS is accompanied by a dedicated software with graphical user interface to control, visualize, and annotate the acquisitions directly in the field and a post-processing library to produce the profiles. The following processing steps are applied: (1) subtraction of the dark current and normalization by the integration time as in  Picard et al. (2016) , (2) precise estimation of the depth at which each spectrum has been acquired using the small difference of timestamps between the depth and spectrum acquisitions, and (3) normalization by the photosensor current.
This function has an offset to represent the uncertainty when the difference is null, i. e. , for λ > 600 nm. Note that since γ (λ) spans several orders of magnitude in the visible, we compute the difference and average in logarithm scale and choose a log-normal distribution for the prior. Formally it reads log γ ice (λ i ) ∼N log γ (2008) ice (λ i ) + log γ (1984) ice (λ i ) 2 | log γ (2008) ice (λ i ) -log γ (1984) ice | + σ 0 , (14) where σ 0 is the standard deviation (in logarithm scale) when the difference between both datasets vanishes. Figure  2  shows this prior distribution along with IA1984 and IA2008 data. The spread is very large at wavelengths shorter than 600 nm (weakly informative prior), with possible absorption coefficient extending beyond the range of the 1984 and 2008 datasets.
In contrast, the uncertainties at the longer wavelengths are determined by σ 0 = 0. 23, which is equivalent to ±25 % relative uncertainty. The statistical problem so stated is solved for the 70 homogeneous zones and 29 wavelengths from 320 to 880 nm (20 nm step). Some deep zones have no data for the longer wavelengths because the measurements are under the noise level. The problem is huge with a total of 1417 unknowns and 40 261 observations. The computation of the posterior distributions is performed using a Markov chain Monte Carlo sampler called No-U-Turn Sampler (NUTS;  Hoffman and Gelman, 2014). We use the software package pymc3 (https: //github. com/pymc-devs/pymc3), which is easy to use and efficient even without expert tuning. We run eight independent chains from which 20 000 samples are drawn.
In contrast, at shorter wavelengths we found a large discrepancy exceeding 1 order of magnitude between our estimate and both IA1984 and IA2008. The wavelength of minimum absorption also significantly varies: 390, 430, and 470 nm, respectively, for IA2008, our estimate, and IA1984. No conclusion in favor of any dataset can be drawn at this stage. We applied the WBG method individually to all the homogeneous zones, which yields 70 ice absorption spectra (Fig. 5 ). Most spectra follow the same general trend as the previous example, featuring a slight decrease from UV to a minimum reached in the visible around 400-450 nm and an increase at longer wavelengths. The scatter is relatively small at long wavelengths (550 nm and longer) but reaches nearly 1 order of magnitude near the absorption minimum.
A few spectra seem to be outsiders and/or noisy. This is probably because the zone thickness and measurement quality is variable throughout the dataset. The quality depends on several factors, including the depth of the zone, snow homogeneity, quality of the hole, etc. It is therefore incorrect to interpret the set of curve as representative of the uncertainty. The different curves are not equiprobable. Nevertheless, none of the spectra reaches either IA1984 or IA2008 in the range 320-500 nm. We also note that most values at wavelengths longer than the reference (600 nm) are slightly lower than IA1984 and IA2008. A first conclusion of this work is that using the same method as  Warren et al. (2006)  on a larger set of observations, we obtain very variable ice absorption and no agreement with the other datasets at wavelengths shorter than about 550 nm.
The curve set is comparable to the estimate obtained with WBG on the profile 25kmE_1 and to the average of all the zones in Fig. 5 , showing that neither estimation method, BAY or WBG, has a major influence on the conclusion. All the results with our data converge to a minimum absorption around 2 × 10 -2 m -1 located in the range 400-450 nm. The influence of site where the profile has been measured is investigated by applying the BAY method on groups of profiles based on the distance and direction to the station (Figs. 7  and 8 ). It is worth noting that the number of homogeneous zones (and observations) in each subset is very different, which calls for a cautious interpretation. Overall the inter-group differences are small compared with the discrepancy with IA1984 or IA2008. However, they are in some cases significant compared to the posterior distribution width, which means these differences are statistically significant.
Therefore, we propose to update IA2008 with a higher absorption value in the visible and UV. For this, and to reduce the risk of including potentially contaminated sites, we computed another estimate using only the sites at 6 km or more from Concordia station and by excluding the downwind, northern, direction. This new estimate, called "clean", is based on 46 homogeneous zones, which is still large compared to previous studies. It appears to be very close to the one including all sites (Fig. 17 ), which highlights that this selection is only a precaution without significant quantitative impact. Table  S1  in the Supplement provides mean and standard deviation of the samples drawn from the posterior and data are available from http://lgge. osug. fr/~picard/ice_ absorption/. More importantly than adopting one estimate or another, we recommend that modelers systematically account for the large uncertainty on ice absorption that persists even after the present study.
Acknowledgements. This study was supported by the  ANR  program  1-JS56-005-01  MONISNOW. The authors acknowledge the  French Polar Institute (IPEV)  for the financial and logistic support at Concordia station in Antarctica through the  NIVO program. The computations have been done on the CIMENT cluster. The authors thank  S. G. Warren  and  R. E. Brandt  for discussions about their experimental setup and their comments on an earlier version of the manuscript. The editor and two anonymous reviewers are acknowledge for their suggestions to improve the manuscript, especially regarding the differences between the various ice absorption estimates. M. Dumont  is also acknowledged for her comments on the initial manuscript. Edited by: F. Dominé Reviewed by: two anonymous referees.
We implemented the SNF design principles into an appropriately modified version of the Click  (Kohler et al. , 2000)  framework. To demonstrate SNF's performance, we compare it against the fastest Click variant to date, called FastClick  (Barbette, Soldani & Mathy, 2015). To show SNF's generality we tested its performance in three uses cases: (i) a chain of software routers, (ii) nested network address and port translators (NAPTs)  (Liu et al. , 2014) , and (iii) access control lists (ACLs) using actual NF configurations taken from Internet Service Providers (ISPs)  (Taylor & Turner, 2007). Our evaluation shows that software-based SNF achieves 40 Gbps, even with small Ethernet frames, across long (up to 10 NFs), stateful chains. In particular, it achieves up to 8. 5x more throughput and 10x lower latency with 2-3.
FastClick extensions We implemented SNF in C++11. The modules depicted in Fig. 2  are 14,376 lines of code. The integration with FastClick required another 1,500 lines of code (modifications and extensions). Although FastClick improves a router's throughput and latency, it lacks features required for broader NFV applications; therefore, we made the following extensions to target a service-oriented platform: Extension 1: Stateful elements that deal with flow processing such as IP/UDP/TCPRewriter were not originally equipped with FastClick's accelerations such as computational batching or cache prefetching. Moreover, these elements were not designed to be thread-safe, hence they could cause race conditions when accessed by multiple CPU cores at the same time. We 2 This extension is not a direct part of FastClick, since the compressed classification rules are computed by SNF beforehand; then, SNF uses these rules as arguments when calling FastClick's Classifier or IPClassifer elements.
Extension 3: We developed a new element, called IPSynthesizer, in the heart of our execution model shown in Fig. 1. This element implements per-core stateful flow tables that can be safely accessed in parallel allowing multiple TCUs to be processed at the same time. To avoid inter-core communication, thus keeping the per-core cache(s) hot, we extended the RSS mechanism of DPDK (see Fig. 1 ) using a symmetric approach proposed by  Woo & Park (2012). Extension 4: To make software-based classification more scalable, we implemented the lazy subtraction algorithm introduced in Header Space Analysis (HSA)  (Kazemian, Varghese & McKeown, 2012). With this extension, SNF aggregates common IP prefixes in a filter and applies the longest one while building a TCU, thus producing shorter traffic class expressions.
Testbed We conducted our experiments on six identical machines each with a dual socket 16-core Intel R Xeon R CPU E5-2667 v3 clocked at 3. 20 GHz. The cache sizes are: 2 × 32 KB L1, 256 KB L2, and 20 MB L3. Hyper-threading is disabled and the OS is the Ubuntu 14. 04. 1 distribution with Linux kernel v. 3. 13. Each machine has two dual-port 10 GbE Intel 82599 ES NICs. Unless stated otherwise, we use two machines to generate and sink bi-directional traffic using MoonGen  (Emmerich et al. , 2015) , a DPDK-based traffic generator. MoonGen allows us to saturate 10 Gbps NICs on a single machine using a set of cores, while receiving the same amount of traffic on another set of cores. To gain insight into the performance of the service chains, we measure the throughput and end-to-end latency to traverse the chains, at the endpoints.
We use FastClick as a baseline and compare FastClick against SNF (which extends FastClick). We create service chains that run natively in a single process using RSS and multiple CPU cores, as this is the fastest FastClick configuration. We follow two different setups for our software-based and hardware-assisted deployments as follows: Software-based SNF: In the 'A chain of routers at the cost of one,' 'Stateful service chaining,' and 'Real service chain deployments' sections we stress different purely softwarebased NFV service chains that run in one machine following the execution model of Fig. 1. This machine has 4 10 GbE NICs connected to the two traffic source/sink machines (two NICs on each machine), hence the total capacity of the NFV machine is 40 Gbps. The goal of this testbed is to show how much NFV processing FastClick and SNF can fit into a single machine and what processing limits this machine has.
A chain of routers at the cost of one This first use case targets a direct comparison with the state of the art. Specifically, we chain a popular implementation of a software-based router that, after several years of successful research contributions  (Dobrescu et al. , 2009; Han et al. , 2010; Kim et al. , 2012; Sun & Ricci, 2013; Martins et al. , 2014; Barbette, Soldani & Mathy, 2015) , achieves scalable performance at tens of Gbps. As we show in this section, a naive chaining of individual, fast NFs does not achieve high performance. To quantify this we linearly connect 1-10 FastClick routers, where each router has four 10 Gbps ports (hence such a chain has a 40 Gbps link capacity). The down-pointing (green) triangular points in Fig. 7  show the throughput achieved by these chains versus the increasing length of the chains, when we inject 60-bytes frames, excluding the cyclic redundant check (CRC).
152. 0. 0/16) or to the Internet. For the latter traffic, the third NF interconnects the ISP with the Internet by performing source and destination NAPT. We use the above ACLs to generate traces of 64-byte frames that systematically exercise all of their entries. The generated packets emulate intra-ISP, inbound and outbound Internet traffic (see Fig. 9 ). Figure  10  presents the performance of the 3 chains versus the different frames sizes (64, 128, 256, and 1,500 bytes). We implemented the chains in FastClick and a purely software-based SNF using the full capacity of our processor's socket (i. e. , 8 cores in one machine), symmetric RSS, and a batch size of 128 packets. Figure  10A  shows that the small ACL (251 rules), executed as a single FastClick instance, achieves satisfactory throughput, equal to its synthesized counterpart.
LIMITATIONS We do not attempt to provide a solution that can synthesize arbitrary software components, but rather target a broad but finite set of middlebox-specific NFs that operate on the entire space of a packet's header. SNF makes two assumptions: (1) An NFV provider must specify an NF as an ensemble of abstract packet processing elements (i. e. , the NF DAG defined in the 'Service chain configuration' section). We believe that this is a reasonable assumption, followed also by other state of the art approaches, such as Click, Slick, and OpenBox. However, if a middlebox provider does not want to share this information, under non-disclosure or via a licensing agreement, then SNF can synthesize the middleboxes before and after this provider's middlebox. This is possible by omitting the processing graph of this middlebox from the inputs given to the Service Chain Configurator (see the 'Service chain configuration' section).
RELATED WORK Over the last decade, there has been considerable evolution of software-based packet processing architectures that realize wireline throughputs, while providing flexible and cost effective in-cloud network processing. Monolithic middlebox implementations. Until recently, most NFV approaches have treated NFs as monolithic entities placed at arbitrary locations in the network. In this context, even with the assistance of state of the art OSs, such as the Click-based ClickOS  (Martins et al. , 2014)  together with fast network I/O  (Rizzo, 2012; DPDK, 2016)  and processing  (Kim et al. , 2012; Kim et al. , 2015b; Barbette, Soldani & Mathy, 2015)  mechanisms, chaining more than 2 NFs leads to serious performance degradation as stated by the authors of both ClickOS and NetVM  (Hwang, Ramakrishnan & Wood, 2014).
DPIaaS  (Bremler-Barr et al. , 2014)  reuses the costly deep packet inspection (DPI) logic across multiple instances. RouteBricks  (Dobrescu et al. , 2009)  exploits parallelism to scale software routers across multiple servers and cores within a single server, while PacketShader  (Han et al. , 2010)  and NBA  (Kim et al. , 2015b)  take advantage of cheap and powerful auxiliary hardware components (such as GPUs) to provide fast packet processing. All of these works only partially exploit the benefits of sharing common middlebox functionality, thus they are far from supporting optimized service chains. Consolidation at the individual function level is the next level of composition of scalable and efficient NF deployments. In this context, Open Middleboxes (xOMB) by  Anderson et al. (2012)  proposes an incrementally scalable network processing pipeline based on triggers that pass the flow control from one element to another in a pipeline.
The xOMB architecture allows great flexibility in sharing parts of the pipeline; however, it only targets request-oriented protocols and services, unlike our generic framework. Slick  (Anwer et al. , 2015)  operates on the same level of packet processing as SNF to compose distributed, network-wide service chains driven by a controller. Slick provides its own programming language to achieve this composition and unlike our work, it addresses placement requirements. Slick is very efficient when deploying service chains that are not necessarily collocated. However, we argue that in many cases all the NFs of a service chain need to be deployed in one machine to effectively dispatch processing across cores in the same socket. Slick does not allow all of the NF elements to be physically placed into a single process.
Therefore, SNF implements the lazy subtraction technique proposed by  Kazemian, Varghese & McKeown (2012). The benefits of this technique are stated in the 'FastClick extensions' section. Finally, the authors of OpenBox did not stress the limits of the OpenBox framework in their performance evaluation. An input packet rate of 1-2 Gbps cannot adequately stress the memory utilization of the OBIs. Moreover, there is limited discussion related to how OpenBox exploits the multi-core capacities of modern NFV infrastructures. In contrast, in the 'A chain of routers at the cost of one,' 'Stateful service chaining' and 'Real service chain deployments' sections we demonstrated how SNF realizes complex, purely software-based service chains at 40 Gbps line-rate. This is possible by exploiting multiple CPU cores and by fitting most of the data of an entire service chain into those cores' L1 caches.
In this paper we first propose a state of the art on the methods for the visualization and the interpretation of textual data, in particular of scientific data. We then shortly present our contributions to this field in the form of original methods for the automatic classification of documents and easy interpretation of their content through characteristic keywords and classes created by our algorithms. In a second step, we focus our analysis on the data evolving over time. We detail our diachronic approach, especially suitable for the detection and visualization of topic changes. This allows us to conclude with Diachronic'Explorer, our upcoming tool for visual exploration of evolutionary data.
Introduction Databases of scientific literature and patents provide volumes of significant data for the study of scientific production. These data are also very rich and so complex. Indeed, the textual content of the publications, keywords used for archiving in these databases, the citations they contain and affiliations of the authors are as much information that it is possible to exploit for studying corpora of publications. These corpora are therefore a boon for the analysis of scientific and technical information. In this article, we focus in particular on a major concern, which is to identify major changes related to developments in science and to describe them in a textual and visual way. Indeed, monitoring the development of transversal themes as well as detection of emerging themes or bridges between themes allows researchers to ensure of the innovative character of their area of research.