dataesr/scientific-paragraphs-categorization huggingface dataset:
The impact of the inclusion of LHC A F B data is clear. The reason why LHC can improve on the LEP determination of the Zuu couplings is that the latter suffers from large correlations. The rest of this work is organized as follows. In Section 2 we describe the SMEFT theory setup, and in Section 3 we review the constraints obtained from "traditional" pole data (mainly LEP, but not only). Section 4 represents the main element of this work. The pp → + -FB asymmetry is introduced and used to extract bounds on the Zqq vertex corrections, which are then compared with LEP bounds. Finally, Section 5 contains our conclusions.
Traditional pole observables The traditional Z-pole observables are those carried out at LEP-1 and SLC, which we list in Table  1. The experimental values are taken from the classic report of Ref. [4] , with minor modifications affecting the Z width, the hadronic cross section σ had and the FB asymmetry of b quarks to take into account the recent results of Refs. [28, 29]. We also include the measurements of A s and R uc  [9, 30]  that, despite being less precise, are needed to remove flat directions in the EFT parameter space. In the case of the W boson, the traditional pole observables were carried out at LEP-2, Tevatron and LHC, and they are summarized in Table  2. We stress that this dataset includes recent LHC results concerning the leptonic branching ratios of the W boson  [12, 14, 31].
5 ± 0. 4 0. 04 ± 0. 06 -0. 001 ± 0. 005     . ( 4. 11) As anticipated, only a single direction, which we denote as t , is constrained at 0. 5% level. The constraints on the directions orthogonal to t are much weaker, and those on x are practically void within the EFT validity regime. This means that the ATLAS data alone cannot give us useful constraints on all Z boson couplings to quarks. Nevertheless, the input from ATLAS will be invaluable once combined with the other probes of the Zqq couplings, as we will quantify in the next subsection.
In Eq. (4. 12) this is reflected by the large correlation between y and t, which was zero before the inclusion of the A F B data. This is illustrated in Fig. 2 , which shows the impact of the inclusion of the FB asymmetry on the z and t combinations of vertex corrections. Beyond the numerical impact on the extracted bounds, the inclusion of the FB asymmetry in the analysis strengthens the robustness of the EFT approach. In fact, the inclusion of (δg) 2 corrections in the analysis would have a major impact on both LEP and LHC results since O(1) corrections cannot be excluded by each dataset separately. However, such large corrections are not anymore possible once both datasets are used together. This is clearly illustrated in Fig. 2. The importance of A F B data is even clearer in simple New Physics scenarios where only some Zqq couplings are modified.
D0 measurement As mentioned in Section 3, Ref. [38]  included in their dataset the determination of the light quark couplings to the Z boson at D0  [39]. This allowed them to lift the flat direction that appears when only "traditional" pole observables are used. Such determination of the Fig. 2 : Allowed regions (corresponding to ∆χ 2 = 1) for the z and t combinations of vertex corrections, see Eqs. (3. 9) for their definition. The blue vertical band is obtained using LEP data and the rest of traditional pole observables discussed in Section 3, whereas the purple and orange ellipses add the FB asymmetry measurements at LHC and D0 respectively (see main text for details). The left panel is obtained marginalizing over the remaining EFT parameters, cf. Eq. (2. 6), whereas the right panel is obtained setting all of them to zero.
Finally, it would be interesting to analyze the potential of the High-Luminosity LHC  [60]  and other future colliders for improving the constraints on Zqq vertex corrections. We expect that these future studies will increase even further the impact of hadron colliders on the electroweak precision program. into a likelihood for the Wilson coefficients in the Warsaw basis at the scale µ = m Z. In this step, the map should be used for V → I, since our fit results are formally valid in this limit. That can be evolved to other RG scales using e. g. the public code DsixTools  [62]  or Wilson  [63]. Our general likelihood can always be restricted to more constrained flavor scenarios  [64, 65]  or to the universal scenario with oblique parameters  [66].
Acknowledgments VB is supported by by  Ministerio de Ciencia, Innovación y Universidades, Spain  [grant  FPU18/01340 ]. AF has received funding from the  Agence Nationale de la Recherche (ANR)  under grant  ANR-19-CE31-0012  (project  MORA ) and from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  860881-HIDDeN. MGA is supported by the  Generalitat Valenciana (Spain)  through the  plan GenT program  ( CIDEGENT/2018/014 ).
Most text-based programming languages found in introductory programming courses use English words. This fact alone can deter non-English speakers who wish to learn to program: how can we expect them to learn a programming language if they do not even understand the meaning of the keywords they are manipulating? In addition, the syntax and semantics of programming languages are also known causes of learners' mistakes. In this paper, we highlight these difficulties and then present PseuToPy, a programming language which can be localized in several tongues on the one hand and produce instructions close to these natural languages on the other. PseuToPy is still a work in progress: we have developed a version in French and hope to study its use in an educational context to see whether or not programming beginners find it easier to learn programming by implementing algorithms in their native tongues.
This observation is striking for two reasons. The first reason, quite obviously, concerns the fact that a design decision potentially puts aside a large part of the world's population. The second reason is related to the conclusion drawn by Qian and Lehman  [5]  that English proficiency seems to be a strong predictor of students' performances in introductory programming courses. In this paper, we introduce PseuToPy: a programming language aimed at producing almost natural instructions while also providing support for multiple different tongues. This is done by designing a formal grammar based on the Python grammar specification and editing its rules by adding keywords in the targeted languages. As a result, PseuToPy allows learners to write instructions that resemble natural language sentences in their native tongue if supported.
PSEUTOPY 2. 1 Design of PseuToPy The design of PseuToPy follows five aspects: (1) the formal grammar of PseuToPy is based on the grammar specification for Python, (2) this formal grammar is modified to add alternative instructions in another language, (3) the alternative instructions can help disambiguate symbols or emphasize on programming concepts, (4) the instructions produced resemble almost grammatically-correct natural language sentences, and (5) instructions written in PseuToPy can be converted into Python code and then ran to show learners the results of its execution. The first two aspects present the benefits of offering a complete compatibility with Python while allowing for the construction of instructions in a language other than English. For example, if we consider an implementation in French, the two following instructions would be identical: "x = True" and "assigner à x la valeur vrai" 1.
With this example, we can also see the interest of the third aspect. The "=" symbol is a common source of misconception found in the literature  [4]  which, in PseuToPy, can be replaced to emphasize on the actual meaning of this statement: a variable assignment. As we can also see with the example above, PseuToPy can also produce instructions that are close to natural language sentences while complying to the strict rules of a formal grammar. And finally, these instructions can be converted into the equivalent Python program and then ran to allow learners to see the Python implementation and check the correctness of their algorithms.
Implementation of PseuToPy The implementation of PseuToPy is a three-step process. First, Pseu-ToPy specifies its formal grammar and implements a parser in order to produce a syntax tree. This is done thanks to the lark-parser Python package 2. The second step consists in taking this syntax tree and generating the equivalent Python instructions. And finally, the last step consists in modifying this grammar specification by adding keywords in the targeted language. For example, the assign rule illustrated in Fig. 1  shows on its first line the Python rule and on the second line the equivalent rule with French words. With this example, we can also easily see how other languages could be supported in PseuToPy, which we have done for Chinese and Arabic on the assign rule as proof of concept  [2].
These two languages were selected to determine whether PseuToPy could work with non-ASCII characters and with right-to-left languages. At the time of writing, PseuToPy is still under active development and a preliminary version of the French grammar has been implemented. In particular, we have targeted grammar rules which specify instructions that beginners would commonly write. This comprises arithmetic and boolean operations, comparisons, variable assignments, control structures, and function definitions and calls. In the future, we wish to provide support for more statements such as class definition and object instantiation. PseuToPy is an open source project which the authors see as a community effort. It is only with the help of a diverse group of researchers and educators that more languages can be supported.
CONCLUSION AND FUTURE WORKS PseuToPy wishes to offer the opportunity to any non-English speaker to learn to program, using their own native tongue. This task is ambitious, clearly. This is why this article is also a call to the community interested in broadening access to programming to contribute to this project by proposing a grammar specification in their native non-English language. In parallel to this community effort, our next step consist in experimenting with learners in educational contexts with two research questions in mind. First, would the use of a natural programming language help learners to produce less syntax errors? And second, what would be the effects of using a natural programming language on the design and implementation of algorithms by programming beginners? With the first research question, we hope to improve the design of PseuToPy and its underlying grammar specifications.
As a first step towards providing an answer to this question, we plan to adopt a design-based research methodology to specify the French keywords to be used in the grammar with the help of researchers, experts, and practitioners. We then hope to conduct experiments in ecological settings to determine if using one's native tongue reduces the risk of making syntax errors when writing programs. With the second research question, we then wish to study the possible effects of using PseuToPy to learn programming and algorithmic notions. In particular, we plan to conduct a comparative study with French high school students, whose English proficiency should be good enough to be introduced to either PseuToPy or Python, to evaluate how fast or how easily they convert their algorithms into written programs and if we can distinguish learning gains between the two configurations.
FsQCA Due to the skewness of the study constructs (common in large-N settings), the calibration procedure was performed using percentiles to avoid producing less significant outcomes PR  (Pappas et al. , 2017). Based on the results of the probability density function and on Beynon et al. 's procedure (2016), the three qualitative anchors were selected for each construct full-set inclusion (95th percentile), crossover point (50th percentile) and exclusion (5th percentile). Scores were subsequently transformed into set measures based on the qualitative anchors  (Ragin, 2008). The calibrated scores were bound to the thresholds of full membership, the crossover point, and full non-membership (fuzzy score 5 0. 95, 0. 50, 0. 05; respectively). Then, with the current version of the fsQCA software  (Ragin, 2006)  a truth table of 2k rows was produced (Table  2 ), with k representing the number of outcome predictors and rows all possible combinations.
Extending prior research, these findings suggest that focusing on isolated job characteristics to promote innovation has a minor value. Instead, if organizations want to promote IWBs, they should design balancing job characteristics matching the presented configurations. Limitations and future research Although this study makes several significant contributions, it has some limitations that need to be carefully addressed in future research. First, all data were collected with selfreporting measures, this was done because, for both innovative work behavior and job characteristics, the use of supervisor reports may not necessarily be appropriate. Employees have more direct information about the specificity of job tasks as well as the extent to which they have generated, proposed and realized their ideas in the organization  (Janssen, 2000).
3) AGENT As it happened in  [1] , an extra wireless interface is added to each of the APs for monitoring purposes. This avoids the need to periodically use the main interface for that aim, which would interrupt the service to the associated STAs. The previous version made use of Click Modular Router, that allowed the implementation of the main module of the agent. In the current version, the Click module has been substituted by a standalone user-space application called OdinAP, programmed in C. The rest of the elements of the system have been maintained, including the ports for communicating with the controller, the wireless interfaces, etc. The main reason for avoiding Click Modular Router is that it has its own 802. 11 elements, which do not incorporate the latest capabilities of the standard.
5) SECURITY CONSIDERATION It should be noted that security information (including temporal keys) is exchanged between the controller and the APs. This increases the attack surface, since the wired connections between the controller and the APs must also be secured now, and the same happens with the database of the controller. We consider that the protection of the information in the wired network is out of the scope of the present work, assuming that the exchange can be protected by means of proper protocols (e. g. TLS for TCP flows and DTLS for UDP ones), and the database can be protected by normal procedures.
2) EQUIPMENT USED AS STAS The next devices have been used as STAs: • One Mini PC with Kali Linux, running wpa_supplicant, 7  which implements WPA key negotiation with a WPA Authenticator and EAP authentication with an Authentication Server. In addition, it controls the roaming and IEEE 802. 11 authentication / association of the wireless LAN driver. • An unmodified Samsung Galaxy GT-i9505, with Wi-Fi 802. 11 a/b/g/n/ac, dual-band, has also been employed in some tests.
1) HANDOFF IN A MIXED SCENARIO (2. 4 AND 5GHZ) One of the requirements was that the mobility should also be possible among Wi-Fi bands (2. 4 GHz and 5 GHz). In this section we show a capture illustrating that this is possible. Two mini PCs are used as APs: mini PC 1 is in channel 52, using 802. 11ac. It uses two Alfa wireless cards. mini PC 2 uses 802. 11n in channel 9. Its main interface is an internal ath9k card, and it also has an auxiliary Alfa card, able to scan in both bands. The controller is a Raspberry Pi running the controller with a mobility management and load balancing application called SmartAPSelection. 8 The STA is a Samsung mobile phone. When the application orders a handoff from channel 52 to channel 9, beacons with the Channel Switch Announcement element appear in both cases (see the Wireshark captures in the corresponding figures): 1) Mini PC 1 sends CSAs in the 5 GHz band (channel 52), ordering the STA to switch to channel 9 (Fig.
3) ROUND TRIP DELAY In this experiment we want to estimate the delay introduced by the LVAP solution. For that aim, a number of ping messages are sent from a connected STA, to a computer in the wired network (which is in fact the router/DHCP server, see Fig. 10 ). The contribution of the wired segment to the delay is considered as very small. The STA that connects is a mini PC with Kali Linux, running wpa_supplicant. The experiment consists of starting the AP, starting the controller, connecting a STA and sending a number of pings to the router / DHCP server. The Round-Trip Time is estimated as the average of a number of ping messages (40 by default). In Table  3  we present the results using different equipment for running the AP: a mini PC, a PC Engines APU and a Netgear R6100, with and without security.
ERA5 reanalysis The newest-generation ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) is the successor of the previous ERA-Interim reanalysis  (Dee et al. , 2011). ERA5 is now available from 1979 to 2020 with production lagging real time by about 2 months. For the production of ERA5, 4D-Var data assimilation of the ECMWF Integrated Forecast System (in cycle CY41R2) was used  (Hersbach et al. , 2020). The horizontal resolution is about 30 km (T639). In the vertical, the pressure range from the surface to 0. 01 hPa is covered with 137 hybrid levels. The output frequency for ERA5 data is hourly. Due to a cold bias in the lower stratosphere, the reanalysis data have been replaced for the 2000-2006 period with the updated data set named ERA5. 1  (Simmons et al.
The calculation of cross-isentropic vertical velocity for ERA-Interim was described by  Ploeger et al. (2010). In the following, we illustrate the similar (but not identical) procedure for ERA5. Temperature tendencies are only available from the ERA5 forecast data, which is stored twice per day (06:00 and 18:00 UTC) with forecast steps ranging between 1 and 18 h (1 h increments). These temperature tendencies have to be interpreted differently compared with ERA-Interim, as they are "mean rates", representative of the interval between the actual time and the previous post-processing time. For instance, the forecast data at 06:00 UTC with a 5 h forecast step are the mean tendency between 10:00 and 11:00 UTC. Temperature tendencies are provided in K/s (Kelvin per second). The CLaMS preprocessor assigns the forecast variables to the reanalysis data set at the later time (here 11:00 UTC).
This induces a time uncertainty of 0. 5 h, which is negligible for the purpose of this paper. Here, we did not use the hourly ERA5 data to drive model transport; data subsampled in time are used instead. Therefore, the temperature tendencies have to be averaged appropriately, as they represent accumulations since the previous post-processing time and not since the last forecast date. Otherwise, sampling errors will occur. Hence, for the present case of using only 00:00, 06:00, 12:00, and 18:00 UTC data, we average all tendency data sets within 6 h windows around each date. For instance, the mean tendency averaged over 10:00-15:00 UTC (from forecast data at 06:00 UTC with 4-9 h steps, see above) is assigned to the reanalysis data at 12:00 UTC.
The 30-to 40-year time series is presumably still too short for computing long-term trends in reanalysis, where stratospheric variability might be larger compared with climate models. However, studying variability on shorter timescales (e. g. inter-annual or decadal) in reanalysis is of value in itself. Whether the long-term mean age trend in the ERA5 reanalysis is indeed negative needs to be further analysed in the future by including more years after 2018 as well as more years before 1979 (as available from an extended ERA5 data product to be released soon). As already mentioned in Sect. 2. 2, we also carried out CLaMS model simulations with the preceding data set with an existing bias in stratospheric temperatures (termed ERA5. 0 here). Mean age time series from this sensitivity simulation are included in Figs.
Acknowledgements. We are particularly grateful to  Nicole Thomas  for programming support and help with setting up the CLaMS model simulations with the new ERA5 reanalysis. We further thank  Rolf Müller  for comments on an earlier version of the paper, the  ECMWF  for providing reanalysis data, and  Simon Chabrillat  and an anonymous reviewer for their comments on the submitted version of the paper. This study was funded by the  Helmholtz Association  (under grant no. VH-NG-1128 ;  Helmholtz Young Investigators Group A-SPECi ). Finally, we gratefully acknowledge the computing time for the CLaMS simulations which was granted on the JURECA supercomputer at the  Jülich Supercomputing Centre (JSC)  under the  VSR  project  ID JICG11. Financial support. This research has been supported by the  Helmholtz Association  (grant no.
HDR+ is an image processing pipeline presented by Google in 2016. At its core lies a denoising algorithm that uses a burst of raw images to produce a single higher quality image. Since it is designed as a versatile solution for smartphone cameras, it does not necessarily aim for the maximization of standard denoising metrics, but rather for the production of natural, visually pleasing images. In this article, we specifically discuss and analyze the HDR+ burst denoising algorithm architecture and the impact of its various parameters. With this publication, we provide an open source Python implementation of the algorithm, along with an interactive demo.
Introduction Image noise is a common issue in digital photography, and that issue is even more prominent in smaller sensors used in devices like smartphones or action cameras. Continuous improvement in the processing power of small devices has enabled many advances in computational photography, where algorithms are used to compensate for the limited physical properties of the imaging system. Algorithms that aim at removing digital noise are a very recurrent topic of the computational photography literature. Most of them only use the information stored in the input image (they are usually referred to as "single-frame" algorithms), while others suggest the use of information stored 1 https://doi. org/10. 5201/ipol. 2021. 336 2 https://github. com/amonod/hdrplus-python in additional images (usually videos or bursts).
Our Simplified Pipeline Since we decided to mainly focus on the denoising part of the HDR+ algorithm for this article, we implemented a simpler finishing pipeline. It still features parts of Google's own solution, and it is presented as a proof of concept, to showcase some of the decisions they made when designing their pipeline. It does not aim for parity with Google in terms of visual quality, as it would require a considerable amount of additional development time and a lot of trial and error, especially given the relatively sparse description of these steps in the original article. Using rawpy, a Python wrapper of the LibRaw library 10  we transform a temporally and spatially denoised 16 bit Bayer array (even though smartphone raw images typically have a 10 or 12 bit precision, they are often stored on 16 bit arrays) into a 16 bit linear RGB image of the same resolution with the following operations: • Black level subtraction • White balance: rawpy uses the gains stored in the As Shot Neutral Exif metadata tag in the.