dataesr/scientific-paragraphs-categorization huggingface dataset:
The plug-ins developed so far are rather simple, with the noticeable exception of the tube guitar amp simulator, but all are made of multiple Web Audio nodes. It's pure "JS audio coded from scratch, not compiled or generated", contrarily to FAUST or WAMs. The "audio processor part" API of each pedal board plug-in is a subset of FAUST and WAM API methods and properties, with sometimes slightly different names. The definition of plug-ins as Web Components is appealing as components could easily be made available on a remote server and would not conflict with the host code (HTML id attributes, JS names, CSS rules, etc. ) As a proof of concept, two WAMs have been repackaged as Web Components and imported in the pedal board: the Yamaha DX7 FM/PM and the OBXD virtual analog synthesizers 14. This is shown in Figure  5.
The code for packaging the remote DX7 synth is shown in Figure  6. We also managed to import a simple headless FAUST plug-in (the Zita_rev1 reverb 15 ) by wrapping it as a Web Component HTML file (reusing the JavaScript code that loads the WASM code and implements its audio processor API). For this last example, we built a GUI in the HTML template section of the Web Component. What did we learn here? First, we need to have an "audio processor" API that can handle both a single node audio processor (e. g. AudioWorkerNode in WAM and FAUST implementations)as well as multiple nodes (i. e. pedal board plug-in implementation), and expose methods and properties as closely as possible to the those of the standard AudioNode interface. In addition, we should have methods and properties for describing a plug-in (name, version, author etc.
P3: readonly property JSON descriptor A plug-in exposes its parameter space via a descriptor array. Each 14 http://www. webaudiomodules. org/wamsynths/ 15 https://faust. grame. fr/libraries. html#dm. zita_rev1 item in the array holds key, type, range, default, unit, label and normalized flag fields. Type is a hint for generic GUI control type, following the approach used by FAUST.
Guidelines: distributing and reusing plug-ins The Web Components W3C standard 16  defines a way to easily distribute components with encapsulated HTML/CSS/JS/WASM code without namespace conflicts. Where native plug-ins needed to be downloaded and installed, URLs make no difference between a local or a distant plug-in, a Web Component plug-in could be used remotely just by its (possibly RESTful) URL reference. This makes writing a plug-in remote server easy. URLs also enable an intuitive distribution strategy for both headless and GUI-equipped versions of a plug-in. A headless version is distributed as a (minified) JavaScript package (plugin. js), whereas its complete GUI version is encapsulated into an HTML import (plug-in. html). Repositories and aggregator sites can collect plug-in URLs and expose their collections as "cloud plug-in folders".
A series of continuum visible and NIR spectro-interferometric observations by the NPOI, CHARA/MIRC and VEGA instruments covering the whole orbit of β Lyr A acquired during a two-week campaign in 2013 were complemented with UBVR photometric observations acquired during a three-year monitoring of the system. We included NUV and FUV observations from OAO A-2, IUE, and Voyager satellites. All these observations were compared to a complex model of the system. It is based on the simple LTE radiative transfer code SHELLSPEC, which was substantially extended to compute all interferometric observables and to perform both global and local optimization of system parameters. Several shapes of the accretion disk were successfully tested -slab, wedge, and a disk with an exponential vertical profile -and the following properties were consistently found: the radius of the outer rim is 30.
1996; Škoda 1996). RVs were measured independently on four spectral lines Si II 6347 Å, Si II 6371 Å, Hα, and He I 6678 Å. 2. Via an automatic comparison of the observed and synthetic spectra in the PYTERPOL program  (Nemravová et al. 2016 ). 1 The latter method produced slightly less scattered RVs, hence only these are presented here. However, the RVs obtained by both methods are listed in Table  C. 1. At the same time, PYTERPOL also estimated radiative properties of β Lyr A: T eff , log g, and the projected rotational velocity v sin i. The red spectra we used (∆λ 6200 -6800 Å) contain numerous telluric lines, which would adversely affect the results of PYTERPOL. We handle the problem using two methods: (i) 1 Å intervals centered on each telluric spectral line were omitted from each spectrum, and (ii) telluric and stellar spectra were separated by spectral disentangling  (Simon & Sturm 1994)  in Fourier space by the KOREL program  (Hadrava 1995 (Hadrava , 1997)).
C. 1. We assumed the solar composition in these fits guided by the results of  Abt & Levy (1976)  from the blue spectra since the red spectra at our disposal do not contain enough spectral lines to estimate the metallicity of β Lyr B reliably. Do β Lyr A and β Lyr B indeed form a physical bounded system? All RV measurements of β Lyr B are plotted in Fig. 2. The rms errors are only available for  Abt & Levy (1976)  and for our new RVs and are shown in this plot. We explain the large scatter of the RVs from the photographic plates by the combination of four factors: moderate dispersion of the plates, variable quality of individual spectra, relatively large projected rotational velocity of β Lyr B, and line blending. It is our experience that due to blends with fainter spectral lines the apparent RVs of individual 1 The program is available at https://github.
The proper motions of β Lyr A and β Lyr B were also investigated. Measurements accessible through VizieR database  ). The former is north-south oriented, and the latter east-west oriented. (Ochsenbein et al. 2000)  at CDS were downloaded. Only values of µ α and µ δ published after the Hipparcos mission were retained. Proper motions in the right ascension were corrected for the declination of both systems (see Appendix C. 3 for details). Their weighted mean is given in Table  5. Individual measurements and the weighted average are shown in Fig. 3. Notes. µ = X 2 µ + Y 2 µ denotes the magnitude of the proper motion vector, and θ = arctan X µ /Y µ the position angle with respect to north, where X µ and Y µ are given by Eqs. (C. 1) and (C. 2). The close similarity of systemic RVs of β Lyr A and β Lyr B reinforce the hypothesis that the two systems formed in the same association or are even physically linked.
Modeling the continuum radiation Our modeling of β Lyr A and its application to spectro-interferometric and photometric observations is presented in the following section. Our models are based on the program SHELLSPEC, developed by  Budaj & Richards (2004). This program was equipped with additional features that simplify modeling of binaries, computations of synthetic interferometric observables, and a solution of the inverse task. First, we briefly outline the program and its new Python wrapper called Pyshellspec. Then, we proceed to the development of several alternative models of β Lyr in continuum and their comparison to observations.
Foundations of the model The model is based on the existing program SHELLSPEC which was designed for the computation of synthetic light curves, spectra and images of stars and/or binaries surrounded by a moving 3D circumstellar medium by means of solving a onedimensional LTE radiation transfer along some user-specified line-of-sight. To provide an environment for solving the transfer, the modeled (non-stellar) objects cannot be represented with a 2-D mesh covering only the photosphere of each object. Instead the grid has to sample the whole volume in 3-D. The only two exceptions are models of stars, which are always opaque, hence their atmospheres form a boundary condition. In SHELLSPEC, each object is placed into an regularly sampled cuboid (divided into n x × n y × n z cells) that represents "the Universe".
Interface for automatic comparison and fitting On output, SHELLSPEC computes the monochromatic flux F ν (in erg s -1 cm - 2  Hz -1 ), and intensity I ν (x, y) (in erg s -1 cm -2 sr -1 Hz -1 ) projected onto the plane perpendicular to the line-of-sight, where x and y are the Cartesian coordinates in this plane, but it does not carry out a comparison of these quantities to observed ones, or automatic optimization of the model parameters. Moreover, individual model components implemented in SHELLSPEC are not bound by any orbit, although some model parameters depend on orbital parameters. The components are almost independent and do not share parameters that are common. To overcome these limitations we wrapped SHELLSPEC in a Python interface. 2.
This is a small subset of all 2392 images (per one iteration) used to derive light curves, interferometric visibilities, closure phases, and triple product amplitudes. temperature profile, because its (not reduced) χ 2 = 114 981 is well above the 3-σ level (that is 105 669) inferred from the best-fit model. The central region of the slab seems too cold, and especially the UV light curves do not match the observations (cf. its χ 2 LC contribution). In case of the wedge, the steady profile is compensated by the central star (gainer), which is partially visible in the opening. The resulting χ 2 Fig. 8. Similar comparison as in Figure  7 , but for squared visibilities |V| 2 , with a contribution χ 2 V 2 = 54 137. The |V| 2 values are plotted against projected baseline B/λ (in cycles), and shifted vertically according to the dataset number.
Acknowledgements. The constructive criticism of an earlier version of this manuscript by the anonymous referee is appreciated. This research was supported by the grants  P209/10/0715 ,  GA15-2112S , and  GA17-00871S  of the  Czech Science Foundation , by the grant no. 250015  of the Grant Agency of the  Charles University in Prague. This work is based upon observations obtained with the  Georgia State University Center for High Angular Resolution Astronomy Array at Mount Wilson Observatory. The  CHARA Array  is supported by the  National Science Foundation  under Grant No. AST-1211929 ,  AST-1411654 ,  AST-1636624 , and  AST-1715788. Institutional support has been provided from the  GSU College of Arts and Sciences  and the  GSU Office of the Vice President for Research and Economic Development.
We thank  M. Zhao  for participating in the MIRC observations, and  B. Kloppenborg  for his support in the earlier attempts to use the SIMTOI modeling tool. Two Reticon spectra of β Lyr B were obtained by  Dr. P. Hadrava. HB  acknowledges financial support from the  Croatian Science Foundation  under the project  6212  " Solar and Stellar Variability ". The work of JB was supported by the  VEGA 2/0031/18  and  APVV-15-0458  grants. We acknowledge the use of the electronic database from the  CDS, Strasbourg , and the electronic bibliography maintained by the  NASA /ADS system. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (http://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, http://www. cosmos. esa. int/web/gaia/dpac/consortium).
Development The writing studio work group tested a number of online collaborative LaTeX writing tools, and considered ways of adapting these tools to the specific needs of A&A. The key requirements at that time were: the usability of the interface, the ability to promote the A&A brand, and the ease of integrating A&A specific tools and services into the interface. The working group chose ShareLaTeX and a prototype was built using the ShareLaTeX opensource code supported under the open-source license that it uses. All changes and revisions were published to a Github public repository. In addition Dropbox sync functionality was provided under a license agreement to make this available to all users of the writing studio. The tool underwent rigorous user-testing, gathering feedback from editors and beta testers to help design the best solutions and features to ensure the best user experience.
2013). A portion of the trajectory of every orbit is shown in Fig. 1  in a cylindrical MSO coordinate system. Only the M1 flyby passed near the equator (xy-plane), while the D113 and D182 orbits, similar to other nominal orbits of MESSENGER in 2011, passed over the poles with a closest approach of ∼200 km over ∼60 • northern latitude of Mercury  (Solomon et al. 2007). As shown in Fig. 1 , the selected orbits cover different areas of the magnetosphere including the equatorial region (M1), the Sunmidnight plane (D182), and an oblique angle from the midnight meridian (D113). These assure us that our model-data comparison does not only focus on a specific magnetospheric region and its associated phenomenon, but has also been validated against different magnetospheric locations based on the available MESSENGER observations.
Inverse problem approach Due to the lack of an upstream solar wind monitor and no direct observation of the solar wind plasma by MESSENGER, there is no complete information about solar wind plasma parameters, i. e. density, flux, thermal speed, and dynamic pressure, for different passages of MESSENGER throughout Mercury's magnetosphere. Therefore, we take an inverse problem approach using the AMITIS code to infer the solar wind plasma parameters upstream of Mercury based on magnetic field observations by the MAG instrument on MESSENGER. For every orbit shown in Fig. 1 , we take the average of the observed magnetic fields outside the magnetospheric disturbances of Mercury and apply it to our model as the only known parameter in our simulations (for further details, see Sect. 2. 3). Then we perform ∼40-50 simulations for each orbit with various solar wind dynamic pressures, and compare the magnetic fields from our simulations with those obtained from MAG instrument along the trajectory of MESSENGER to find the best agreement between our simulations and observations for the location of the magnetospheric boundaries (bow shock and magnetopause), and the overall intensity and orientation of the magnetic fields (for further details, see Sects.
g. 30 min)  (Winslow et al. 2012 (Winslow et al. , 2013)). However, for the selected orbits in this study, especially for D113 and D182, we did not find considerable variations between one-hour and 30min averages of the IMF. Therefore, for the orbits analysed here, we take a one-hour average of magnetic field data observed by the MAG instrument before (after) MESSENGER moved into (out of) Mercury's magnetosphere, except for the z-component of the IMF, B z , during the M1 flyby. Taking a one-hour average of B z before the inbound and after the outbound bow shock crossing during the M1 flyby gives a southward IMF with B z ≈ -1. 0 nT. However, the previous analyses of the M1 flyby (e. g. Slavin et al. 2008 Slavin et al. , 2010;; Anderson et al. 2011a ) considered B z ≈ +4 nT by taking the average of the magnetic fields for a shorter period than one hour during the outbound bow shock crossing.
Acknowledgements. All MESSENGER data used here are publicly available at  NASA 's Planetary Data System (PDS). From there we have used the MESSENGER MAG reduced data archive ( MESS-E/V/H/SW-MAG-4-SUMM-CALIBRATED-V1. 0 ) in the MSO coordinate system, file names MAGMSOS-CIAVGXXXXX_10_V08. Data from the modelling results are available upon request to the corresponding author,  Shahab Fatemi  (shahab@irf. seor shahabfatemi@gmail. com). This research was partly conducted using resources provided by the  Swedish National Infrastructure for Computing (SNIC) , project  SNIC 2017/11-15  at the  High Performance Computing Center North  ( HPC2N ),  Umeå University, Sweden.
This paper tackles data selection for training set generation in the context of near real-time pedestrian detection through the introduction of a training methodology: FairTrain. After highlighting the impact of poorly chosen data on detector performance, we will introduce a new data selection technique utilizing the expectation-maximization algorithm for data weighting. FairTrain also features a version of the cascade-of-rejectors enhanced with data selection principles. Experiments on the INRIA and Caltech datasets prove that, when fine trained, a simple HoG-based detector can perform on par with most of its near real-time competitors.
Introduction Human detection is the keystone of many computer vision applications that depend on the reliability of this important first step. These applications, ranging from surveillance, queue monitoring, retail data mining, and automatic pedestrian detection in the automotive industry, have fueled research during the last decade, leading to a soaring number of approaches on the topic. In a recent study  [7] , features, context, feature data, and the combination of approaches have been identified as the key performance elements for pedestrian detection. Roughly 30% of approaches focus on developing, combining or adapting features, and this direction has lead to most of the breakthroughs over the past years, such as channel features  [20] , or convolutional network  [48]. Context adaptation  [34] , harnessing frequent geometry  [38]   [53]  or environment patterns  [50]   [69] , also successfully received attention from the community, but this algorithm adaptation is often application or even dataset dependent.
However, less progress has been stated concerning feature data. Training sets are still generated by applying the same techniques than a decade ago: Increasing the data variability is done by blindly mixing datasets  [7] , and Bootstrapping  [18]   [30]  remains among the preferred choices for training set generation. In this paper, we argue that data selection and adaptation is an important area that deserves more attention from the community. Training for pedestrian detection is, indeed, a peculiar task. It aims to differentiate a few positive samples with relatively low intra-class variation and a swarm of negative samples picturing everything else present in the dataset. Consequently, the training set lacks discrimination and is highly imbalanced. Due to the possible creation of noisy data while oversampling, and the likely loss of information when undersampling, balancing positive and negative instances is a rarely addressed issue in the literature.
Experiments carried out on the INRIA  [18]  and Caltech-USA  [25]  datasets, demonstrate the effectiveness of the approach, leading a simple HoG-based detector to outperform its near realtime competitors. The rest of this paper is organised as follows. Section 2 reviews the related state-of-the-art. Section 3 gives an overview of the system. Section 4 covers our training set generation technique whereas section 5 details our classification scheme. Section 6 reports our experiments and section 7 concludes on this work.
Related Work Data selection from imbalanced datasets has been a concern for pedestrian detection since the birth of the field. [40]  proved that disproportioned datasets degrade SVMs prediction accuracy, especially for non-linearly separable data. Subsequent research on these experiments  [70]  showed that best performance was obtained for approximately comparable class cardinalities when oversampling the minority set. Limited work carries out these issues. Bootstrapping  [18][30]  is probably the most common adaptation to the problem. This data training mechanism improves accuracy thanks to successive training set updates focusing on samples difficult to classify. It is first initialised on uniformly drawn negative samples. At each iteration, the negative training set is augmented with the false positives of the previous run.
This cascade-of-rejectors  [73][8]  can also be considered to speed up the detection. The technique, inspired by Viola & Jones face detector  [61] , builds up a classifier cascade that consists of successive rejection stages that get progressively more complex, therefore rejecting more difficult candidates as the classifiers get more specialised. [58]  further upgraded the principle from features to data by performing hierarchical clustering on the dataset, leading to improved classification results. However, it requires, for each new datum, to find the corresponding set of clusters it belongs to. See  [37]  for more details on learning from imbalanced datasets. The training procedure that this paper advocates for unfolds as follows. After the initial data selection, we balance the negative and positive sample cardinalities.
Training Set Generation Let us run a simple experiment to illustrate our training set generation main idea. Performance should theoretically asymptotically increase with the validation set size. This assumption, depicted by the dashed curve on the Figure, corresponds to the common belief that the dataset informative power is directly correlated to its size. However, the results, plotted with the plain curve, are quite different from our expectations. They show significant irregularities as the dataset size increases; and the largest datasets do not even provide the best results. This encourages us to think twice about the way training data are considered: Apt data selection may be more important than their simple addition. This is the principle underpinning our training set generation. The method is composed of several stages, detailed within the body of this section.
Cascade-of-Rejectors The cascade-of-rejectors is an efficient strategy for sieving out a large proportion of false positives, and has been applied several times in the context of pedestrian detection  [17] [73]  [8] [58]  [61]. However, tuning this cascade of classifiers remains problematic. First, the classifiers are prone to errors that propagate along the cascade. Second, most authors employing this technique solely reject negative data after each classification step to avoid the creation of classifiers likely to reject positive data during the subsequent stages. However, this leads to progressively more imbalanced datasets. Fine tuning of the original positive versus negative instances ratio is usually necessary to obtain satisfactory results (a 2 to 4 times bigger negative set is the common practice).
But this strategy is still under-optimal as balanced datasets are proved  [70]  to be the best performing ones. To deal with these issues, we propose a new cascade-of-rejectors. Our approach borrows  [39] 's main idea with a two-stage classification: The first one aims for a high recall whereas the second one focuses on precision. The first stage embodies a cascade of n -1 rejectors. To avoid increasing the false negative rate, these n -1 initial classifiers enforce a high recall, moderately optimizing the performance on the precision. The metric formalises as: M = (T N × W T N + T P )/(P + N ) (3) with P, N, T P, T N , and W T N being respectively positive, negative, true positive, true negative instances, and the weight associated to T N. W T N is set at W T N = 0. 5. Its parametrisation has no influence on the results, when set low enough, which enforces a perfect or near perfect recall.
Negative data, with a confidence below 0. 5, are rejected at each stage. Negative data oversampling is then undertaken to avoid emptying the validation set. When insufficient to match the positive instances cardinality, random positive data under-sampling is then considered. We employ Adaboost as classifiers for this part. These decision trees have demonstrated competitive performance whilst retaining computational efficiency  [67]   [45]. During testing, typically over 95% of the data are safely discarded after 4-5 iterations. This cascade-of-rejectors guaranties a high recall, increased performance induced by balancing the datasets at each iteration, and few parameters to tune. The second stage performs a finer classification, aiming for high precision results. Since the bulk of the data have been removed, more demanding computation can be performed at this stage without slowing down the detector.