dataesr/scientific-paragraphs-categorization huggingface dataset:
2021). Systematic errors are plotted as vertical bars and are 15% of the nominal values. Random errors represent around 5%. each bin and then find the point in the streamline model closest to it. Then, the distance r bin is the 3-dimensional distance between that point and the protostar. We express this distance as the free-fall timescale from r bin using: t bin = - 0 r bin dr v 2 r,0 + 2GM tot 1 r -1 r 0 , (3) where v r,0 is the initial velocity (1. 25 km s -1 ) at r 0 (3300 au) from the streamline model toward the direction of the protostar. The integral is done numerically using the python package scipy function integrate. The difference between the solution of Equation 3 and the free-fall timescale given by the streamline model is less than 20 yr, which is negligible for the timescales we are working with.
Why are mass and infall rate lower limits? The estimated mass of the streamer (see Sect. 3. 5) is a lower limit because of observational limits in our data and the assumptions made in the mass calculation. We estimate the length of the streamer as 3300 au, using H 2 CO emission and the streamline model. This is possibly not the full length of the streamer for three reasons. First, the H 2 CO emission is cut off by the primary beam of the NOEMA observations (  22 ), and our observations are not sensitive to strong emission beyond this radius. Second, there is a strong offset emission toward the southwest of Per-emb-50, located just outside the primary beam at ∼ 3000 au, seen in all of the molecular tracers used in this work (see Figs. 1  and 5 ). Moreover, there is significant C 18 O emission observed in the SMA MASSES program  (Stephens et al.
Acknowledgements. The authors would like to thank the anonymous referee for their detailed suggestions, which helped improve the final version of this paper. The authors also thank the  IRAM  staff at the  NOEMA observatory  for their invaluable work making these observations possible. M. T. V. would like to thank  L. Testi  for his valuable discussions and comments. M. T. V. , J. E. P. , P. C. and D. S. -C. acknowledge the support by the  Max Planck Society. D. S. -C. is supported by an  NSF   Astronomy and Astrophysics Postdoctoral Fellowship  under award  AST-2102405. A. F. thanks the  Spanish MICINN  for funding support from  PID2019-106235GB-I00. D. S. acknowledges financial support by the  Deutsche Forschungsgemeinschaft  through  SPP 1833 : " Building a Habitable Earth " ( SE 1962/6-1 ).
I. J. -S. has received partial support from the  Spanish State Research Agency (AEI)  through project number  PID2019-105552RB-C41. N. C. acknowledges funding from the  European Research Council (ERC)  via the  ERC Synergy Grant  ECOGAL (grant  855130 ). M. T. acknowledges support from project  PID2019-108765GB-I00  funded by  MCIN/ AEI /10. 13039/501100011033. This work is based on observations carried out under project number  L19MB  with the  IRAM NOEMA Interferometer. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). This research made use of Astropy, 1 a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013Collaboration et al. , 2018)).
Upwelling over BoxMK To our knowledge, upwelling over BoxMK, referred to as MKU hereafter, has not been detected and studied so far. It was however already visible in AVHRR SST data for 1991-2004 (see Fig. 1  of  Da et al. , 2019)  and in SODA reanalysis SST for 1987-2008 (see Fig. 5  of  Fang et al. , 2012). The summer surface cooling over BoxMK can actually be observed in OSTIA and COPERNICUS analysis data but with much warmer values than in SYMPHONIE (Fig. 3 ). This can be explained by the scarce satellite cover over the MKU area and the smoothing associated with the construction of reanalysis data. The percentage of days during which JAXA data are available is shown as an example in Fig. 1d  for summer 2018: data availability never exceeds ∼ 85 % over the SCS, is ∼ 75 %-80 % over BoxOF, and is lower than 60 % over BoxMK.
To determine if SST cooling really occurs over BoxMK, we thus examine in situ and high-resolution satellite data that cover BoxMK during the period of upwelling. First, the R/V ALIS crossed BoxMK in June 2014. A surface cooling over BoxMK was indeed observed by ALIS-TSG on 25 June 2014 and simulated by SYMPHONIE, with values of minimum SST ∼ 28. 2 • C (Fig. 4a, b ). On this day, OSTIA and COPERNICUS analysis show a significant cooling in the same area as SYMPHONIE (Fig. 4c-e ). However, minimum SST does not go below ∼ 28 • C in analysis data, whereas it reaches ∼ 27. 5 • C in SYMPHONIE. mum SST during the summer period over BoxMK. Figure  6  shows the SST maps during those three upwelling peaks. A surface cooling over BoxMK is produced by JAXA and SYMPHONIE during those peak periods, with SST < 28 • C.
, 2005; Lawrence and Bariel, 2010). The Marque River in Northern France is classified as a river with a high anthropogenic impact and is declassified as "bad status" for both ecological status (because of nutrients and oxygen) and chemical status (because of polycyclic aromatic hydrocarbons, some pesticides and industrial substances) according to the European Water Framework Directive (WFD) (data available on https://www. eau-artois-picardie. fr/). Understanding the anthropogenic pressures exerted on the Marque River through a study along the river course is also of interest to improve the management of this watercourse, given that a nonnegligible contribution of WWTPs is suspected. It is particularly crucial during dry periods. A sampling campaign along the Marque River was therefore performed in July 2019 with the aim to determine the chemical compositions (major, trace and ultra-trace elements) as well as stable water isotopes during a low water period.
Data treatment, statistical treatment and principal component analysis (PCA) ANOVA tests were performed to highlight significant differences between concentrations observed in WWTPs discharges (n = 7) and in the watercourse (n = 14). A p-value of 0. 001 was chosen for significance due to the low number of sampling points with the aim to ensure the robustness of the statistical analysis. These results were obtained by using R software with the R Commander interface. The R Commander interface was conceptualized by  Fox (2005). A hierarchical classification was also carried out for an overall overview of the sampling points and to highlight outlier points. In addition, FactoMineR package was used to perform PCA analysis. The description of this package and its use are available in the literature  (Lê et al.
Conclusion Through statistical analyses and the use of several tracers (mostly EC, Rb, Cl -, Gd, As, B, Cr, Cs, Ho, K, Na, Rb, Sn, Sr, Y and to a lesser extent Pt), the large impact of WWTPs onto the Marque River elemental composition has been demonstrated. This conclusion was also confirmed based on flow calculation thanks to the data obtained from the WWTPs owners and the flows recorded by two monitoring stations along the river watercourse. The evolution of various parameters was monitored and PCA analysis showed that the first dimension, related to WWTPs discharges, represented around 40% of the total variability. The PCA treatment also allowed determining which elements are positively or negatively correlated with the presence of WWTPs confirming results already obtained in the literature with the strong correlation between WWTPs discharges and the presence of elements such as Gd and B.
Acknowledgments ICP-OES and ICP-MS measurements were performed on the  Chevreul Institute Platform (U-Lille/CNRS ). The  Region Hauts de France  and the  French government  are warmly acknowledged for the co-funding of these apparatus. The study was carried out in the framework of the CPERs CLIMIBIO and ECRIN projects with the same funders. The authors wish to thank the  European Commission  funding for the  LIFE   RUBIES  project ( LIFE20 ENV/000179 ). The authors would also like to warmly thank Noréade and  Lille Metropole  ( E. Bugner & C. Mouillet ) for having allowed us the access to the WWTPs discharges data as well as the  Artois-Picardie Water Agency  ( D. Bolzan ) for the constructive and helpful discussions.
Today we observe amazing performance achieved by Machine Learning (ML); for specific tasks it even surpasses human capabilities. Unfortunately, nothing comes for free: the hidden cost behind ML performance stems from its high complexity in terms of operations to be computed and the involved amount of data. For this reasons, custom Artificial Intelligence hardware accelerators based on alternative computing paradigms are attracting large interest. Such dedicated devices support the energy-hungry data movement, speed of computation, and memory resources that MLs require to realize their full potential. However, when ML is deployed on safety-/mission-critical applications, dependability becomes a concern. This paper presents the state of the art of custom Artificial Intelligence hardware architectures for ML, here Spiking and Convolutional Neural Networks, and shows the best practices to evaluate their dependability.
I. INTRODUCTION Machine Learning (ML) has proven to give very good results for many complex tasks and applications, such as object recognition in images/videos, natural language processing, satellite image recognition, robotics, aerospace, smart healthcare, and autonomous driving  [1] -  [4]. Unfortunately, nothing came for free. Indeed, the hidden cost behind ML performance stems from their high complexity in terms of operations to be computed and the involved amount of data. The direct consequence is the need of high performance computers, long execution time (especially during training) and high power consumption. For example, the famous AlphaGo  [4]  required 4 to 6 weeks of training executed on a machine composed of 2,000 CPUs and 250 GPUs consuming about 600kW. The direct consequence is that the computational workload is limited, in particular for embedded platforms, by the two well-known walls of computing architectures:  (1)  The memory wall due to the increasing gap between processor and memory speeds, and the limited memory bandwidth making the memory access the killer of performance and power for memory access dominated applications;  (2)  The power wall as the practical power limit for cooling is reached, meaning no further increase in CPU clock speed.
Several Fault Injection (FI) tools have been proposed to emulate faults in GPUs. GPU-Qin  [55]  exploits the CUDA-GDB debugger for NVIDIA devices to inject single bit-flips in the registers exposed by the Instruction Set Architecture (ISA); the solution is quite sophisticated and presents a 100x slowdown w. r. t. nominal execution. A similar debugging-based approach is used by CAROL-FI  [56]  which acts at the source code-level to inject both bit-flips and random values. CAROL-FI presents less than a 5x execution time degradation and the benefit of performing both the injection and the error propagation at the same level of the source code. However, the approach limits the injection sites to the variables. An alternative approach is adopted in SASSIFI  [57]  and LLFI-GPU  [58] : the source code is instrumented for FI before being executed on the GPU.
SASSIFI, proposed by NVIDIA, is able to corrupt all registers of the ISA by means of several injection modes, with a 5x slowdown as reported in  [56]. LLFI-GPU uses a similar approach while acting at instruction level, claiming a speedup w. r. t. GPU-Qin about 42x on the execution time and similar benefits as CAROL-FI in terms of the analysis of the effects of the errors. Finally, in other works  [50] ,  [53] , FI is performed again at the software level by manually modifying the application code to integrate the corruption facilities. All these tools are based on complex modification and recompilationof the target code to enable FI, thus leading to considerable performance degradation. More recently, the new NVIDIA tool, called NVBitFI, has been proposed  [59]. This tool overcomes several limitations of previous tools by performing a dynamic and selective code instrumentation.
A similar strategy but integrated in other ML frameworks is the one presented in  [52] ,  [61] ,  [62] , integrated in the Keras, Pytorch and Caffe frameworks, respectively. Finally, a cross-layer approach, implemented in PyTorch, where the CNN is executed via software and the fault injection is carried out by switching down to the Hardware Description Language (HDL) description of the processing unit has been proposed in  [63]. The most recent contributions, from which our proposal moves the steps, are Fidelity  [64]  and BinFi  [65]. The former defines a set of error models adherent to what is observed after FI experiments and it then analyses the effects of the faults in terms of the Architectural Vulnerability Factor (AVF). On the other hand, the latter identifies the safety-critical bits in the considered ML application, relying on TensorFI to carry out fault injection in the operators.
Therefore, our proposal bridges the gap between fault injection and error simulation, exploiting the advantages of both approaches. The first step of the flow is dubbed the "Operators Extractor" that extracts all the operators from the considered CNN. For each identified operator a standalone test program, namely "Experiment Instance", is generated in Caffe. The program transmits input tensors to the GPU, executes the operator on that device, and retrieves the output tensor. These programs represent the code that will be executed in the FI experiments. The subsequent step of the flow is the actual fault injection. Since we target CNNs accelerated on GPUs, FI environments specifically meant for this processing platforms, e. g. , SASSIFI or NVBitFI, are suitable for our flow. Due to the complexity of modern GPUs and to the size of the their memory, an exhaustive FI campaign is unfeasible.
Retrieve of Flicker data Wood  (Gosal et al. 2019b )describe Flickr as a popular photo-sharing service with more than 70 million members and 200 million photos shared globally. It is not clear how many Flickr users are based in or have visited the study area, but the website is likely to have a reasonable market share in the country when compared to its most similar competitors, such as Photobucket and Panoramio. Instagram looks to be considerably more popular than Facebook, but it cannot be used since the application-programming interface (API) prohibits automated data processing without user authorization (Instagram, 2014). We develop Python scripts as a rst step to retrieve the data that the Flickr web site hosts. We have developed a script in the Python environment with Flickr's Application Programming Interface (API) to facilitate repeatable requests to the Flickr API.
The functions of this approach make a call to the Flickr API and return both the raw photographs and the additional metadata stored in a CSV table. The feature of this Python script is that it provides a reproducible way to access geolocated photographs via search queries, making it possible to provide useful datasets for a range of ecological analyses. The script feature allows users to de ne a set of search criteria that are then queried against the Flickr database. A block of data containing the metadata of the photographs corresponding to the search criteria is then returned. The ability to re ne search parameters allows a more targeted approach to using Flickr's geolocated photographs by returning only those that are relevant to the research. The package also includes features for uploading photos (link URL S, URL O, URL SQ.
) and collecting user information. In our case study, we used Bounding BOX coordinates 20. 7076 N, 55. 2352 E, 21. 9485 S, 56. 102 W. The images were downloaded with associated metadata, including longitude and latitude, the date and time the photograph was taken, and the user ID of the photographer. A total of 29,000 images uploaded by 2456 users between 2017 and 2021 were downloaded. The limitations of the package created in the Python environment usually turn to the level of photo searches via the Flickr API, which will return only 4,000 unique results per search criterion, which will limit the ability to easily access data for spatially or temporally large searches. For this purpose, it is recommended that the request must contain a deadline and a maximum date (min date taken and max date taken).
Convolutional neural networks (CNNs) Deep learning algorithms are mostly based on arti cial neural network architectures with more hidden layers, so they are called "deep neural networks. " Deep learning algorithms" are part of a larger group of machine learning algorithms. They can make machines "learn" based on their past experiences to do the job they were given, which makes them better at the task. It works well with a large dataset and can extract the features on its own. They don't usually require human intervention in this process. Deep learning algorithms require highly con gured machines, normally GPUs, as they require a long time to be trained. Among the three main architectures used to create deep learning models, the convolution Neural Network (CNN). A.
In order to monitor the training, we used Early Stopping as a trigger to stop the training process based on the validation loss metric with a patience of 9, once the chosen performance measure stops improving. However, the model at the end of training may not be the model with best performance on the validation dataset. In this regard, we managed to use Model Checkpoint, which monitors the training based on in order to keep the model that has achieved the best performance. Testing: In order to measure the performance of the trained model, we relied on the testing phase in which we used 20% of the data. Choosing an appropriate measure is generally di cult in applied machine learning, but is particularly di cult for unbalanced classi cation problems. First, because most standard measures that are widely used assume a balanced class distribution such as accuracy.
Image classi cation model results Training: As already mentioned, we trained the CNN architecture using the loss functions as a supervised metric for the early stopping process, in addition to the performance metrics mentioned above. As shown in (Fig. 3 ), we notice that the loss function curve is decreasing from 3. 5 to 2 in the rst epoch, although the noisy movements from epoch 1 to 4 seem to atten when it reaches 21 epochs. A continued training of a good t will likely lead to an over t, as a result the training stopped at epoch 29. In this context, we notice that the performance metrics stopped improving after epoch 25 scoring respectively, 99. 16%, 99. 98%, 99. 16% and 98. 54% in accuracy, auc, precision and recall. Testing: In order to evaluate the performance of our model, we used a new dataset to validate the progress of the algorithm's training and optimize it for improved results.
Data analysis After setting up the CNN model for image classi cation, all the photographs extracted from the Flickr database have been grouped into the classes above (data categorization section). The data analysis provided a summary of the overall number of images and users for each CES, as well as the percentage representation of each category. Then, in order to de ne the distribution of images within the research region, we conducted a temporal and spatial analysis (Fig. 2 ) using the date of each photograph taken and the corresponding geographical coordinates. ArcGIS 10 for Desktop was used to conduct spatial studies (ESRI,2011). The proportion of total images captured by year and month was evaluated in temporal analysis. Spatial evaluations were conducted in order to better understand the general distribution of cultural ecosystem services.
2012 Miyazaki et al. , 2018;; Furusawa et al. 2018; Komiyama et al. 2018 ) and the CFHT Large Area U-band Deep Survey (CLAUDS;  Sawicki et al. 2019). This paper is one in a series of papers from twin programs dedicated to scientific results on high-redshift galaxies based on the HSC-SSP survey data. One program is our luminous Lyman break galaxy (LBG) or dropout galaxy studies, named Great Optically Luminous Dropout Research Using Subaru HSC (GOLDRUSH;  Ono et al. 2018; Toshikawa et al. 2018; Harikane et al. 2018a). The other program is highredshift Lyα emitter (LAE) studies using HSC narrowband filters, named Systematic Identification of LAEs for Visible Exploration and Reionization Research Using Subaru HSC (SILVERRUSH;  Inoue et al. 2018; Konno et al. 2018; Ouchi et al. 2018; Harikane et al.
2018b Harikane et al. , 2019;; Shibuya et al. 2018a Shibuya et al. , 2018b;; Higuchi et al. 2019; Kakuma et al. 2021; Ono et al. 2021). Our new LBG catalogs are made public on our project webpage  23  or Zenodo. 24 his paper is organized as follows. We show the observational data sets in Section 2 and describe sample selections in Section 3. The results of the UV luminosity functions and clustering analysis are presented in Sections 4 and 5, respectively. We discuss our results in Section 6, and summarize our findings in Section 7. Throughout this paper, we use the Planck cosmological parameter sets of the TT, TE, EE+lowP+lensing+ext result  (Planck Collaboration et al. 2016) : Ω m = 0. 3089, Ω Λ = 0. 6911, Ω b = 0. 049, h = 0. 6774, and σ 8 = 0. 8159. We define r 200 that is the radius in which the mean enclosed density is 200 times higher than the mean cosmic density.
5 diameter aperture. The sky noise is calculated from fluxes in sky apertures that are randomly placed on the images in the reduction process. The limiting magnitudes measured in g, r, i, z, and y bands are presented in Table  1. We select isolated or cleanly deblended sources from detected source catalogs available on the database that are provided by the HSC-SSP survey team. We require that none of the central 3 × 3 pixels are saturated, and there are no bad pixels in their footprint, like the definition of the effective area described above. We also require that there are no problems in measuring the CModel fluxes in the gri images for g-dropouts, in the riz images for r-dropouts, in the izy images for idropouts, and in the zy images for z-dropouts, except for the problem of unnaturally bright magnitudes described above.
e. , sources with redshiftreliability flags of 2,  3, 4, 9, 12, 13, 14, and 19. Here we focus on sources with spectroscopic redshifts of z spec > 3 in these catalogs. In total, 1037 dropouts in our sample have been spectroscopically identified at z spec 3 in our observations and previous studies, including 770 galaxies and 267 AGNs. These sources are listed in Table  10 , and the redshift distributions of the sources are shown in Figure  4. Figure  1  shows the distributions of the spectroscopically identified sources at z spec > 3 in the two-color diagrams. We also plot sources in the UD-COSMOS field with spectroscopic redshifts of z spec < 3 as foreground interlopers. In addition, the tracks of the model spectra of young star-forming galaxies that are produced with the stellar population synthesis code GALAXEV  (Bruzual & Charlot 2003)  are shown.
We do not include Lyα emission in the galaxy SEDs because the line fluxes are typically not significant compared to the continuum in the broadband fluxes. The IGM attenuation is taken into account by using the prescription of  Madau (1995). Different simulations are carried out for the Wide, Deep, and UltraDeep layers by using the SynPipe software  (Huang et al. 2018) , which utilizes GalSim v1. 4  (Rowe et al. 2015)  and the HSC pipeline hscpipe  (Bosch et al. 2018). We insert large numbers of artificial sources into HSC images. Then we select high-redshift galaxy candidates with the same selection criteria, and calculate the selection completeness as a function of magnitude and redshift, C(m, z), averaged over UV spectral slope β UV weighted with the β UV distribution of  Bouwens et al. (2014).