dataesr/scientific-paragraphs-categorization huggingface dataset:
We present results from an analysis of all data taken by the BICEP2 & Keck Array CMB polarization experiments up to and including the 2014 observing season. This includes the first Keck Array observations at 95 GHz. The maps reach a depth of 50 nK deg in Stokes Q and U in the 150 GHz band and 127 nK deg in the 95 GHz band. We take auto-and cross-spectra between these maps and publicly available maps from WMAP and Planck at frequencies from 23 GHz to 353 GHz. An excess over lensed-ΛCDM is detected at modest significance in the 95×150 BB spectrum, and is consistent with the dust contribution expected from our previous work. No significant evidence for synchrotron emission is found in spectra such as 23×95, or for correlation between the dust and synchrotron sky patterns in spectra such as 23×353.
Before the 2014 season two of the five receivers of Keck Array were refitted for operation in a band centered on 95 GHz (the other three receivers remaining unchanged at 150 GHz). In this paper we fold in this new data and perform a multicomponent, multi-spectral likelihood analysis similar to our previous analysis  [16]. This paper builds on the initial BICEP2 results paper [10, hereafter BK-I], the Keck 2012+2013 results paper  [11,  hereafter BK-V], and the BICEP2/Keck/Planck analysis paper  [16, hereafter BKP]. Instrument and observations. -The Keck Array instrument is described in Sec. 2 of BK-V. (See also the BI-CEP2 Instrument Paper  [17]  for further details. ) Before the 2014 observing season two of the receivers of Keck Array were removed, the lenses and filters were replaced with versions optimized for a band centered at 95 GHz, and the focal planes were replaced with units loaded with appropriately scaled versions of our antenna-coupled detectors  [18].
Because the physical size of these antennas is larger each of the four tiles contains only a 6 × 6 array (rather than 8 × 8 at 150 GHz). With two focal planes at 95 GHz this gives 288 total detector pairs (576 total detectors). During the 2014 austral winter season the array was operated exactly as for the previous seasons. A ∼ 1% region of sky centered at RA 0h, Dec. -57. 5 • was observed from March until November over ≈ 4600 fifty minute "scansets". Efficiency and yield was similar to previous seasons. See Sec. 4 of BK-V for further details of the observing strategy and data selection. BICEP2/Keck Maps. -The processing from time stream to maps is identical to that described in Sec. III & IV of BK-I and summarized in Sec. 5 of BK-V. Relative gain calibration is applied between the two halves of each pair and the difference is taken.
-We next proceed to a multicomponent, multi-spectral likelihood analysis which is an expanded version of that described in Sec. III of the BKP paper. We compute the likelihood of the data for any given proposed model using an extended version of the HL approximation  [25]  and the full covariance matrix of the auto-and cross-spectral bandpowers as derived from simulations (setting to zero terms whose expectation value is zero). In this analysis we primarily use a lensed-ΛCDM+dust+synchrotron+r model and explore the parameter space using COSMOMC  [26]. The COSMOMC module containing the data and model is available for download at http://bicepkeck. org. In this paper the "baseline" analysis is defined to: • Use the BK14 maps as shown in Fig. 1  (all BI-CEP2/Keck data up to and including that taken during the 2014 observing season).
Fig. 8  compares signal levels and current noise uncer-tainties in the critical ∼ 80 bandpower (updated from Fig. 13  of BKP). A second season of 95 GHz Keck Array data has already been recorded (in 2015) and will push the 95 × 95 point down by a factor of 2. During 2015 two receivers were also operated in a third band centered on 220 GHz, producing deep maps which will improve dust separation. This 2015 data is under analysis and will be reported on in a future paper. In addition, BICEP3 began operations in 2015 in the 95 GHz band. In this paper, we have presented an analysis of all BI-CEP2/Keck data up through the 2014 season, adding, for the first time, 95 GHz data from the Keck Array. We have updated our multi-frequency likelihood analysis with a more extensive foreground parameterization and the inclusion of external data from the  23   larized bands of Planck.
ACKNOWLEDGMENTS The Keck Array project has been made possible through support from the National Science Foundation under Grants ANT-1145172 (Harvard), ANT-1145143 (Minnesota) & ANT-1145248 (Stanford), and from the Keck Foundation (Caltech). The development of antenna-coupled detector technology was supported by the JPL Research and Technology Development Fund and Grants No. 06-ARPA206-0040 and 10-SAT10-0017 from the NASA APRA and SAT programs. The development and testing of focal planes were supported by the Gordon and Betty Moore Foundation at Caltech. Readout electronics were supported by a Canada Foundation for Innovation grant to UBC. The computations in this paper were run on the Odyssey cluster supported by the FAS Science Division Research Computing Group at Harvard University. The analysis effort at Stanford and SLAC is partially supported by the U.
OMP and Extracellular Protein Preselection. Vaxign (http://www. violinet. org/vaxign/index. php) was used to shortlist proteins with potential as vaccine candidates due to their cellular localization, probability of having adhesin-like characteristics, and number of transmembrane helixes  [25]. Vaxign is a web-based pipeline dedicated to vaccine design and integrating several bioinformatic programs. Subcellular localization is predicted using PSORTb2. 0  [29]. The probability of adhesin characteristics is predicted by SPAAN software  [30]  and the transmembrane helix topology is predicted by HMMTOP  [31]  using a hidden Markov model. Campylobacter jejuni subsp. jejuni 81-176 is available in the Vaxign database of over 350 listed genomes, along with nine other Campylobacter genomes. Extracellular and outer membrane proteins having an adhesin probability score > 0.
Protein Antigenicity. VaxiJen v2. 0 (http://www. ddgpharmfac. net/vaxijen/VaxiJen/VaxiJen. html) was used to predict protein antigenicity. This software uses the physicochemical properties of proteins to predict their antigenicity from FASTA-submitted amino acid sequences. This feature is characterized according to an antigenic score. Proteins with an antigenic score > 0. 5 were selected as described by Doytchinova and Flower  [32].
Epitope B Prediction. BCPreds software (http://ailab. ist. psu. edu/bcpred/) was used to identify B-cell epitopes in FASTA-submitted amino acid sequences. This program provides two methods based on different algorithms: the amino acid pair (AAP) antigenicity method  [33]  and the BCPreds method using string kernels  [34]. These methods predict antigenic linear nonoverlapping 20-mer epitopes from the whole antigen. Each preselected protein was analyzed and B-cell epitopes with a score >0. 8 were accepted (specificity > 80%). The selected epitopes were again submitted to VaxiJen software to check their individual antigenicity and those having an antigenic score >0. 5 were selected. Furthermore, for each protein and each algorithm, the ratio of B-epitopes to protein length was calculated to assess B-epitope density.
Protein Selection according to Antigenicity and Number of B-Epitopes. To refine the selection, the 22 preselected proteins were submitted to the VaxiJen server for antigenicity prediction. Antigenicity scores ranged from 0. 4511 to 0. 7827. This step allowed the elimination of two proteins with an antigenicity score lower than 0. 5 (YP 001000503. 1 and YP 001000297. 1) (Table  1 ). The VaxiJen software indicated that all other candidates were antigenic (score > 0. 5). Each antigenic protein was assessed in terms of Bepitopes using BCPreds and AAP algorithms, and each Bepitope was studied for its antigenicity. Table  1  summarizes the number of B-epitopes predicted for each protein and each algorithm as well as the ratio between the number of B-epitopes and protein length. 2 , all the proteins were shared with available C.
g. ,  Wilson et al. , 2007; Sitas, Baillie & Isaac, 2009; Muter et al. , 2013; Żmihorski et al. , 2013; Kim et al. , 2014; Roberge, 2014). Given that the online media represent one of the major routes through which information related to conservation issues reaches decision makers, interest groups and the public, it is considered that the media coverage intensity has a direct influence on public opinion  (Barua, 2010; Jacobson et al. , 2012; Proulx, Massicotte & Pépino, 2014; Bombaci et al. , 2015). Thus, it is assumed that species coverage by online media indicates public perception of conservation issues and potential biases in public interest, and represents popularity of a species and its public appeal  (Sitas, Baillie & Isaac, 2009; Żmihorski et al. , 2013; Roberge, 2014). At the same time, species coverage in scientific publications and databases is considered to represent available knowledge and scientific attention  (Sitas, Baillie & Isaac, 2009; Jarić, Knežević-Jarić & Gessner, 2015; Fleming & Bateman, in press).
Data mining within different databases and the Internet in general based on species names is increasingly recognized as a valuable tool in conservation research. Studies on species coverage in online media commonly focus on Internet pages, newspaper articles, social media networks and different Internet search engines, while the search is commonly conducted by using species vernacular names  (Barua, 2010; Jacobson et al. , 2012; Bhatia et al. , 2013; Muter et al. , 2013; Żmihorski et al. , 2013; Kim et al. , 2014; Roberge, 2014). However, use of species vernacular names for web-based data search is problematic. Firstly, numerous different names for the same species exist in different languages. Secondly, even within the same language there can be multiple vernacular names for the same species.
Nevertheless, they have been only rarely used for data-mining in scientific studies (e. g. ,  Wilson et al. , 2007; Sitas, Baillie & Isaac, 2009) , probably because it is unclear whether a search using Latin names will produce unbiased results as compared to vernacular names, as one may expect them to be used predominantly in the scientific community. For example, press articles using Latin names are expected to be rare. To our knowledge, this issue has yet to be addressed. Here we assessed the potential use of Latin names as an alternative to vernacular names for data mining within the field of conservation science. We assessed the relationship of search results based on Latin and vernacular names of species from different species groups. This relationship was assessed within different online sources, such as Internet pages, newspapers, social media networks and images posted on the Internet.
METHODS Species lists, with their Latin and vernacular names (specifically English common names), were obtained from the IUCN Red List database  (IUCN, 2015). We focused on four charismatic and endangered animal groups: diurnal birds of prey (i. e. , members of the order Accipitriformes, Falconiformes and Cathartiiformes), Carnivora, Primates and marine mammals (i. e. , cetaceans and pinnipeds). Within each of the four species groups, 20 species were selected for the analyses by stratified random sampling, namely by sampling randomly within two subgroups that comprise species receiving high research attention and those of low scientific focus. Research focus was defined as the number of scientific publications per species  (Jarić, Knežević-Jarić & Gessner, 2015; I Jarić et al. , 2015, unpublished data).
Since there were no date ranges established for collected data, all results dating prior to the time of the assessment (in December 2015) were included in the analysis. Statistical analyses were conducted using the R programming language (R v. 3. 0. 2). For R scripts see Supplemental Information 2 (also available in the online repository, https://dx. doi. org/10. 6084/m9. figshare. 3381073. v2). Since the variables were not normally distributed (Lilliefors (Kolmogorov-Smirnov) test, p < 0. 001), nonparametric tests were applied. Relationship between the search results based on vernacular and Latin species.
Consequently, assessments of species coverage within newspaper articles based on Latin names should be interpreted with due caution. We assessed the two search methods within the five different online media categories that are commonly used by the scientific community for data mining. Twitter and Facebook currently represent the two most popular social networks and powerful research tools  (Miller, 2011; Naaman, Becker & Gravano, 2011; Roberge, 2014; Papworth et al. , 2015). News media are a common venue for broadcasting science topics to the general public, which makes them suitable to reflect public attention and popular attitudes  (Muter et al. , 2013; Veríssimo et al. , 2014; Papworth et al. , 2015). Besides the online media types that are commonly assessed by the scientific community, we also demonstrated the use of the number of images posted on the Internet as an indication of the public attention towards different species.
, 2012). As stated by  Wilson et al. (2007) , the species Latin names are the same in every language, and they are widely used by non-scientists. It is important to emphasize, however, that due caution is needed when using Latin names for data-mining purposes, due to their instability over time  (Lepage, Vaidya & Guralnick, 2014). For instance, substantial numbers of species of birds of prey assessed in the current study are disputed regarding their taxonomic status or nomenclature. Our intention was to use the assessed species groups merely as an illustration of the presented method and to address the question we discussed here. In addition to the IUCN Red List database, other relevant databases and sources such as Avibase  (Lepage, 2016)  should be also consulted to resolve the status of the assessed species.
Figure1Relationship between search results per species based on vernacular and Latin species names, conducted within the four studied species groups and the five assessed sources; axes represent logarithmic scales. Presented data were transformed using x ← x + 1, in order to allow presentation in log-plots of results with the value of zero; for the original dataset, see Supplemental Information 1 (also available in the online repository, https://dx. doi. org/10. 6084/m9. figshare. 3381073. v2).
Field data We used data from the long-term grassland biodiversity experiment in Jena  (Weigelt et al. 2010). In this dataset, 60 plant species were grown in monocultures from 2002 to 2010, with each monoculture replicated in 2 plots. In each monoculture plot, biomass was measured twice a year in June and September, in 2 samples per plot. Half of the monocultures (i. e 60 plots) were given up in 2006. Additionally, 16 mixtures of 2, 4 and 8 plant species, 14 mixtures of 16 plant species and 4 mixtures of all 60 plant species were grown from 2002 to 2008. In each polyculture plot, biomass of all plant species was measured twice a year in June and September, in 4 samples per plot. After each biomass measurement, both monoculture and polyculture plots were mown  (Weigelt et al. 2010). We therefore consider in the following that there are two growing seasons per year, from April to June, and from July to September (Figure  1 ).
Analyzing species demographical characteristics We assessed whether species with larger intrinsic growth rates rmi have also larger demographic σdi² and environmental σei² variances by correlation analyses. We also assessed whether species demographical characteristics (rmi, Ki, σei, σdi) could be predicted from three functional traits: plant vegetative height, specific leaf area (SLA) and leaf dry matter content (LDMC). Trait data were extracted from the LEDA trait database  (Kleyer et al. , 2008). When multiple entries for a given species were available, we used average values.
Introduction   Since   Bowyer's open source release of the self-replicating rapid prototyper (RepRap)  [1, 2] , people from all over the world have been building and improving RepRap 3-D printers  [3]. The existing control system consists of two parts: firmware on the 3-D printer to send the signals through to individual components, and a program on a desktop computer to send instructions (G-code) to the firmware. Current firmware options (Table  1 ) use the same interface with the host, and suffer from two fundamental problems: 1) they are not robust and 2) they require a weak microcontroller to do significant work, thus limiting their functionality. Some firmware solve the latter challenge by requiring a more powerful microcontroller, which limits its applicability. As there is a standard communication format between existing firmware and hosts, there are also many different options for host software including Pronterface  [4] , Cura  [5]  and OctoPrint  [6].
In terms of ease of use, OctoPrint stands out: it provides a web interface to control the printer over a network from any device with a browser. OctoPrint supports monitoring the printer with a webcam, and makes recordings of the print. Another project that is worth noting in detail is Pacemaker  [7]. Pacemaker defines a new protocol that tries to move most computationally intensive tasks from the firmware to the host. This protocol replaces G-code and requires firmware that can handle it. The Pacemaker program itself runs on a host computer, converting G-code into the format understood by the firmware. The format is designed to be backwards compatible, so different firmware and hosts can work together. This only works if new configurations are tested and validated, requiring many users to maintain a unique configuration.
In addition to communicating with each other, the Franklin C Driver communicates with the Franklin Firmware, while the Franklin Python Driver communicates with Franklin Server. During device discovery, before the driver is started, the Franklin Server also communicates directly with the Franklin Firmware. For Franklin to be considered acceptable as a firmware solution, tests were run using the following: Mendel RepRap  [19] , Delta RepRap  [20] , quad delta RepRap (4 vertically stacked quad MOST delta RepRap heads run on a single microcontroller and set of three position stepper motors), OS laser welder  [10] , PCB micromill  [21]  and the open source metal 3-D printer  [22]. The test data obtained from the latter was utilized to further develop the metal 3-D printer design  [23]. Rather than including parts produced via fused filament fabrication, the updated 3-D printer design was constructed entirely from metal parts to reduce damage caused by weld splatter.
Franklin Server Multiple machines are handled concurrently by the Franklin Server. It detects devices when they are connected and starts the Franklin Python Driver for them, which in turn starts its own Franklin C driver. If a device is detected for which a Franklin Python Driver had already been started (that is, one that had lost its connection), the Franklin Server informs this driver of the reconnected machine and it will resume its operation. If this happens before the buffer in the Franklin Firmware runs out, it has no effect on the currently running job. The Franklin Server provides a web interface, similar to Octoprint. (but without a webcam, although this is easily added). This means that any device with a browser, including a tablet or smartphone, can be used to control it. The web interface makes heavy use of Javascript to present all the changes that are reported by the Franklin Server to the user.
Then follow three 32 bit integers and a 32 bit floating point value. For most commands, 18 bytes are all that is required. The commands that may need more bytes (DATA and RUN_FILE) have a larger length value and follow those values with a list of bytes. When a GOTO or PROBE command is received, a reply is sent, which is either OK or WAIT. In the latter case the queue is full and no further GOTO or PROBE commands can be sent until the Franklin C Driver sends a CONTINUE command. The last interface is between the Franklin C Driver and the Franklin Firmware. This interface has unique qualities because it is implemented over a physical serial line that is much slower and unreliable. Because of the large amount of data that needs to be transmitted over this slow interface, the length byte is not sent, and the length of packets must be determined from their content.
Quality control All compiler warnings are enabled for both Franklin Firmware and the Franklin C Driver. Stack protection was disabled because the AVR platform does not support it. Valgrind  [25]  was used to find buffer overflows and use of uninitialized variables in the Franklin C Driver. Franklin has been used extensively on a variety of devices, ranging from a 1-dimensional syringe pump to 3-dimensional printers. Firebug  [26]  was used to find errors in the HTML and Javascript. Franklin Server delivers web pages written in HTML 5 and Javascript (Ecma 262). Care has been taken to follow the standards; no browser extensions have been used. Franklin has been used successfully on a variety of different machines listed above. In addition, Franklin has been used in conjunction with a metal inert gas (MIG) weld-based 3-D printer to develop substrate release mechanisms for 3-D printed parts  [23].
Language All code, comments and documentation are in American English. (3) Reuse potential In its current state, Franklin can be used for controlling 3-D printers and other manufacturing machines to produce research-grade equipment. Such equipment can also be controlled with Franklin. While the web interface is targeted at 3-D printers and similar machines, the code is written in a way that makes it easy to design an alternative interface. For example, a syringe pump could be controlled with an interface that allows programming a sequence of flows in the way that users of syringe pumps expect. Such a program would use the websockets interface of Franklin to control the hardware, while presenting the user with an interface that is more appropriate for the application than the default CNC interface.
Because it is free software users can improve Franklin and the hardware to fit their requirements. Franklin is in active development and the developer can be contacted through github. Possible improvements, which are considered or being worked on include: • G-code parsing is presently time consuming and processor intensive. Performance can be improved through the use of a compiled language such as C++ instead of Python. • One-click printing support like that common for 2-D printers would vastly improve ease-of-use. This functionality is a logical extension to common 3-D modeling packages (Blender, OpenSCAD, FreeCAD, etc. ) and even web browsers e. g. when browsing objects hosted on 3-D printer aggregate sites such as Youmagine. com, 3dprint. nih. gov or Thingiverse. com. • Running a G-code converter (a slicer for 3-D models) as part of Franklin would allow users to send model files directly to Franklin, again improving ease-of-use.
From this linear representation, it is possible to determine a near-to-optimal coding formalism which involves the inversion of a model of the (forward) neural transformation  [22]. As a matter of fact, a normative explanation for the coding of images into activity in the neural tissue is the optimal representation of visual information  [1]. Inspired by the representation in V1, a popular method requires the efficient modeling of images as the sparsest combination of edges and such a coding strategy indeed resembles the neural activity (that is, the spike patterns) observed in V1  [15]. Using such a method, it was previously shown that any image from a database of static, grayscale natural images may be coded using solely a relatively few number of coefficients (See Figure  2  and SI Section 5 for a full description of the algorithm).