dataesr/scientific-paragraphs-categorization huggingface dataset:
We provide a publicly available R code to compute the methods introduced in this work (https://github. com/psBiostat/left-censored-Lasso). It would be interesting to compare the Lasso-regularized Buckley-James least squares method to Lasso-regularized censored LAD method. The Lasso extension of censored LAD has been proposed in different works  [41, 42, [56] [57] [58] [59] [60] [61] [62]. However, to our knowledge, there is no publicly available implementation, and no simple implementation relying on existing packages seem straightforward. Moreover, several works have shown the superiority of maximum likelihood estimation in low-dimensional settings when the Gaussian assumption is valid  [18, [20] [21] [22]. Nevertheless, optimization strategies for complex likelihood functions (such as that in Eq.
Acknowledgements We acknowledge members of the  Standardization and Clinical Relevance of HIV Drug Resistance Testing Project for the Forum for Collaborative HIV Research. We thank "Sidaction, Ensemble contre le Sida", France , for their continuous support. We would like to thank  Binbin Xu , postdoctoral researcher at SISTM research team from  Inserm BPH U1219 & Inria BSO , for his help on testing the R code.
This is already a characteristic of molecular length scales, sufficient in the present case as explicit molecules are not included in the FSBD simulation. The desired simulation output is the real-space interface height function h(r,t), and thus, FS is explicitly filled according to the conjugate symmetry (h k * = h -k ). The key points of the simulation are (i) the initial FS structure of h k (0) is given by a complex Gaussian random variable with zero mean and variance ⟨|h k | 2 ⟩, for which the analytical form is known for a free membrane;  37  (ii) the Fourier components are propagated according to the Euler numerical integration scheme of the first order in the time step  38  (vide infra); and (iii) the real-space height function is extracted with the inverse 2D Fourier transform ∑ = = - -• r h t h t L h t ( , ) [ ( )] 1 ( ) e k k k k r i 1 2 (18) In the ESI-3, we provide details of the implementations (Octave 39 and MATLAB  40  ), together with a verification that the simulation reproduces the analytically known TCF of h(r,t).
■ ACKNOWLEDGMENTS P. H. acknowledges  EU   Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant no. 703446  and discussions within the  COST action   CA15209. J. V. has obtained financial support from the  Academy of Finland  (grant no. 296292 ). The authors acknowledge financial support from the  University of Oulu  ( Kvantum Institute ) and the  CSC IT Center for Science, Finland , for generous computational resources.
The ExSeisDat (Extreme-Scale Seismic Data) set of libraries addresses these problems through the development and implementation of easy to use, object oriented libraries that are portable and open source with bindings available in multiple languages. The lower level parallel I/O library, ExSeisPIOL (Extreme-Scale Seismic Parallel I/O Library), targets SEG-Y and other proprietary formats, simplifying I/O by internally interfacing MPI-I/O and other I/O interfaces. The I/O is explicitly handled; end users only need to define the memory limits, decomposition of I/O across processes, and data access patterns when reading and writing data. ExSeisPIOL bridges the layout gap between the SEG-Y file structure and file system organization. The higher level parallel seismic workflow library, ExSeisFlow (Extreme-Scale Seismic workFlow), leverages ExSeisPIOL, further simplifying I/O by implicitly handling all I/O parameters, thus allowing geophysicists to focus on domain-specific development.
Operations in ExSeis-Flow focus on prestack processing and can be performed on single traces, individual gathers, and across entire surveys, including out of core sorting, binning, filtering, and transforming. To optimize memory management, the workflow only reads in data pertinent to the operations being performed instead of an entire file. A smart caching system manages the read data, discarding it when no longer needed in the workflow. As the libraries are optimized to handle spatial and temporal locality, they are a natural fit to burst buffer technologies, particularly DDN's Infinite Memory Engine (IME) system. With appropriate access semantics or through the direct exploitation of the low-level interfaces, the ExSeisDat stack on IME delivers a significant improvement to I/O performance over standalone parallel file systems like Lustre.
Motivation and introduction Overall resolution and size  (Stanghellini and Carrara, 2017)  of seismic studies have increased of the past several decades; individual files range in size from 100s of GiBs to greater than 10 TB. The high resolution of these studies have allowed for better identification and delineation of shallow anomalies and structures related to hydrocarbons and migration paths, thus leading to more accurate estimations and extractions of petroleum products  (Hustoft et al. , 2007; Lin et al. , 2013). With the increase in file size, geophysicists have increased both the computational and personnel time spent handling and optimizing I/O for these files. While these large and dense datasets have improved seismic interpretation, they have also lead to I/O and memory bottlenecks.
1 ). As a result, neither the trace attributes nor sets of trace data are stored contiguously on disk, even when written in a continuous block of storage. This layout strategy leads to poor cache usage and pre-fetching by the operating system, thus increasing I/O time. In dealing with SEG-Y files, programmers are forced to deal with legacy data formats, e. g. IBM floating point numbers and EBCDIC character encoding, along with keeping track of metadata distributed throughout the file. For serial code, alone, these incur a lot of developer overhead in conversion and tracking. For this reason, most commercial seismic data processing tools, e. g. OpenCPS and Geosoft, use their own, more regular data formats for processing like SEIS and Geosoft Database. A number of tools exist which can convert SEG-Y files to other formats, e.
g. Segpy and ObsPy. However, almost all freely available programs that either convert or process SEG-Y files are serial programs. Since the SEG-Y file system cannot be modified due to legacy code and industrial constraint, the way to address the access pattern issue has to be dealt at the library level. Thus shielding geologists from the complexity of the underlying file system but embedding the necessary logic to provide efficient access. The Extreme-Scale Seismic Data (ExSeisDat) project addresses the I/O and memory bottlenecks associated with seismic processing within the oil and gas sector though the use of state-of-the-art parallel techniques paired with I/O hardware and software technologies. Designed in partnership with Tullow Oil plc and DataDirect Networks (DDN) to be both scalable and user friendly, ExSeisDat includes two open source libraries: ExSeisPIOL, a low-level seismic parallel I/O library, and ExSeisFlow, a high level parallel seismic workflow library.
A burst buffer  (Liu et al. , 2012)  accelerates I/O through the use of Solid State Drives (SSD) that deliver faster read and writes than a traditional Hard Disk Drives (HDD). The underlying operating system later flushes the data to the HDDs when it is convenient. However, these SSDs do not deal any better with random write patterns than HDDs other than their inherent greater speed. DDN's Infinite Memory Engine (IME) addresses the issues of varying read and write patterns not handled by traditional burst buffers with deployment of a software solution in tandem with its caching system. IME intercepts I/O commands to the underlying PFS from the compute nodes like other burst buffers. However, since IME is composed of SSDs, which are flash devices, incoming data must be written to clean memory blocks rather than rewriting over existing data.
Therefore, IME utilizes a logging-based file system where data is written in the order which it is received rather than how that data is stored. IME's software then aligns and orders data for optimal I/O for the underlying PFS. In the case of input data, the IME's SSD cache allows for efficient data access from the PFS and handles all read requests from the compute nodes. The overall I/O time of an application only reflects the amount of time it takes for the compute nodes to read and write to IME not the underlying PFS, like Lustre.
Indeed, for poorly ordered disk reads, parallelisation can actually be a pessimization, rather than an optimization. For older, more established programs in particular, efficient parallel I/O of SEG-Y files would require a significant change in software architecture would incur significant development time. Thus far, many applications have relied on vertical scaling -simply using a larger, faster machine with more memory -to handle larger SEG-Y files. Open source formats, like HDF5  (Folk et al. , 2011)  and Adaptable Seismic Data Format (ASDF;  Krischer et al. , 2016) , and proprietary formats, like SEIS and Geosoft Database, store metadata and trace data in a more contiguous manner. Typically, a seismic data processing program will instead work with its own proprietary format that's more easily parallelized, and expect a geophysicist to convert their trace data to a format supported by the program.
ExSeisDat ExSeisDat is a open source, portable set of object oriented I/O and workflow libraries designed to simplify parallel I/O and parallel file system optimization for seismic files in order to increase application performance and developer productivity. Source code is available at https://github. com/ICHEC/ExSeisDat under the LGPLv3 license. The project aims to alleviate the bottlenecks in I/O both in runtime and in development time. The libraries completely encapsulate the details of the SEG-Y file format while still providing the flexibility for file and trace header customization that is prevalent when working with SEG-Y, abstracting the specific details away from the developer. To increase usability, the libraries include a language binding interface in C, allowing for future development of e.
g. python and matlab bindings. While traditional I/O optimizations libraries such as MPI I/O tend to be blind to the semantic of the application, the ExSeisDat library seeks to bridge the gap between the file format and the purpose of the application and simultaneously address the I/O performance constraints. This domain specific approach has been applied successfully to other application area in HPC, such as FTI (Bautista-Gomez et al. , 2011) a high level checkpoint restart library taking care of the storage architecture, or PDI  (Roussel et al. , 2017). Tailoring the libraries specifically for seismic file formats and applications allows for greater in-depth I/O optimization than would be found in general parallel I/O or parallel file system libraries. It also allows users to focus on domain specific development and optimizations using geophysically intuitive operations.
I/O optimizations are paired with smart caching, targeted at typical geophysical workflows, reducing redundant I/O calls while minimizing memory usage. ExSeisDat also leverages software and hardware solutions to maximize one process reading and writing to one disk. It will communicate and coordinate across processes to coordinate data access as much as possible. As this is not always possible, it also takes advantage of DDN's IME hardware to transform random read and write access to an IME middleware layer into contiguous access to the Lustre layer. This maximizes throughput from the ExSeisDat to IME, and then IME can reorder these accesses to maximize throughput to the filesystem. ExSeisDat contains two core libraries, ExSeisPIOL and ExSeisFlow, that handle explicit file I/O and seismic workflows respectively.
This is repeated until the values are unchanged on all processes for an iteration of the algorithm. In the worst case scenario for this sort algorithm, for N trace data blocks and P processes, each process performs O(N + (N/P) log(N/P)) operations with O(P) communications. The sort utility is ideal for benchmarking the ExSeisDat libraries due to its multiple I/O patterns. With the given stripe size, individual traces are essentially contiguous reads while all file and trace headers are read and written noncontiguously. These varied read and write patterns best showcase the power of ExSeisDat. A sort program using ExSeisFlow API can be written as: int main() { auto piol = ExSeis::New(); Set set(piol, ''input. segy'', ''output. segy''); set. sort(type); return 0; } It is worth noting the sort function is built into ExSeis-Flow, so it is, perhaps, unsurprising the snippet is so short.
, 2017). The benchmarking results presented here show that ExSeisDat is well suited to parallel processing of large files, up to 20 TB in this benchmark. In particular, the performance achieved by a relatively small snippet of code is remarkable. One goal of the ExSeisDat project is that most common operations on seismic data files should require as short a snippet as this, and achieve highly performant code. As other seismic data files tend to be more coherent than SEG-Y files, we expect the simplicity of code and performance to remain at least at this level with the introduction of new file formats to ExSeisDat. Unfortunately, since there are no other existing parallel implementations of SEG-Y I/O library, a direct and meaningful comparison of benchmarks between ExSeisDat and other libraries could not be included in this study.
Video++ Library Video++  (Garrigues and Manzanera, 2014 ) is a library written in C++14 whose purpose is to allow the fast development of high performance video processing applications. It manages memory allocation and alignment, pixel accesses and simple writing of pixelwise and neighborhood operations. By using metaprogramming based on lambda functions and variadic templates, it generates code that is both concise and easy to optimize and parallelize using G++6 and OpenMP. It has been shown  (Garrigues and Manzanera, 2014)  that Video++ was more efficient than OpenCV on local and regular operations, and as fast as the raw OpenMP version, while being much more concise. The dense 1-to-1 HT being essentially regular, and the tracking functions highly parallelisable, Video++ seems a good framework for implementing our algorithm.
As shown in the table 1, our DHT algorithm (row 3) performs better than the implementations of SHT (row 1) and PPHT (row 2) in OpenCV. If needed, it can be made even faster by adding a threshold parameter th on Algorithm 1, by computing the HT only if the magnitude of the gradient is greater than th. This then corresponds to the Semi-Dense HT. We have separately assessed the execution time of each part of the Dense one-to-one Hough Transform: (1) computation of the gradient of the whole image, (2) the HT itself, i. e. computing the parameters and voting (3) the search of the dominant lines. To get relevant results we launched the program 1000 times on each of the three images. For the true Dense HT, i. e. without using a threshold for the gradient, the results can be seen in Table  2. For the Semi-Dense HT (th = 100), results are displayed on Table  3.
Grapevine wood fungal diseases such as esca are among the biggest threats in vineyards nowadays. The lack of very efficient preventive (best results using commercial products report 20% efficiency) and curative means induces huge economic losses. The study presented in this paper is centered around the in-field detection of foliar esca symptoms during summer, exhibiting a typical "striped" pattern. Indeed, in-field disease detection has shown great potential for commercial applications and has been successfully used for other agricultural needs such as yield estimation. Differentiation with foliar symptoms caused by other diseases or abiotic stresses was also considered. Two vineyards from the Bordeaux region (France, Aquitaine) were chosen as the basis for the experiment. Pictures of diseased and healthy vine plants were acquired during summer 2017 and labeled at the leaf scale, resulting in a patch database of around 6000 images (224 × 224 pixels) divided into red cultivar and white cultivar samples.
Then, we tackled the classification part of the problem comparing state-of-the-art SIFT encoding and pre-trained deep learning feature extractors for the classification of database patches. In the best case, 91% overall accuracy was obtained using deep features extracted from MobileNet network trained on ImageNet database, demonstrating the efficiency of simple transfer learning approaches without the need to design an ad-hoc specific feature extractor. The third part aimed at disease detection (using bounding boxes) within full plant images. For this purpose, we integrated the deep learning base network within a "one-step" detection network (RetinaNet), allowing us to perform detection queries in real time (approximately six frames per second on GPU). Recall/Precision (RP) and Average Precision (AP) metrics then allowed us to evaluate the performance of the network on a 91-image (plants) validation database.
Thus, the main contributions of this paper are: • the comparison of those popular methods in modern Content-Based Image Retrieval (CBIR)  [7]  applied to a specific and challenging plant disease problem, from leaf classification to detection of the disease at the plant scale; and • the evaluation of the ability to discriminate esca samples from other leaf symptoms. Esca samples are also divided into three severity subclasses (used during the testing stage) to assess the performances on easy and hard samples. This paper is divided into four main sections. Section 2 describes related works about computer vision methods and examples of agricultural applications. Section 3 describes the experimental design and image labeling steps leading to the creation of a custom database. In Section 4, we tackle the classification of leaf image patches from the databases, using the two distinct above-mentioned feature extraction approaches.
That wealth of local informations can then be aggregated into a compact image representation using bag of visual word-type approaches  [20]. SIFT descriptors are used for diverse agricultural applications such as leaf species classification  [21]. In that case, the descriptors help to distinguish species based on the architecture of leaf venation and shape. In  [22] , species retrieval from a database of roughly 80 species is performed, using a fusion of several features including SIFT and Gabor filter, using HSI colorspace. A smartphone leaf identification system is devised for portable android device in  [23]  using SURF (a fast SIFT variant) features with bag of visual words. Similarly, rice flowering steps can be detected using the same SIFT descriptors  [24]. Spatial grids of SIFT descriptors is also used in that study.
As for disease detection, a set of three soybean diseases are classified in  [25]  on scanned leaves. Best results are achieved using a multiscale grid in the form of the Pyramid Histogram of Words (PHOW) method. Extensive experiments around SIFT variants (including color fusions and different keypoint detectors) are also performed in  [26]  for the classification of flowers pictures from three datasets. In the case of esca detection, SIFT/Bag of Words (BoW) combinations are of particular interest. Esca symptoms can be summarized as local patterns on the leaf (following the five main veins) with oriented soft gradients at different scales, meaning SIFT descriptors are good candidates to extract that information. On the other hand, BoW describes the composition of complex images with many objects and shape, similar to natural grapevine images.
Other techniques based on local descriptors such as Local Binary Patterns  [27] , Gabor Wavelets  [28] , Histogram of Gradients  [29]  and structure tensor  [30]  are also popular methods in the literature. Deep learning methods introduced a new shift in the way we envision features. Convolutional Neural Networks (CNNs) architectures use a network of image filters to extract features from an image  [31]. The weights determining the nature of these filters are learned during the training step. The user only defines the global structure of the CNN. CNNs are successfully used for image classification  [32]  and detection problems  [33]  on huge image databases (e. g. , CalTech101 and Imagenet). The excellent results on these datasets have motivated the use of deep learning for many agricultural applications.
For instance, plant identification is performed in  [34]. In that study, two image databases are considered, one being the above-mentioned Flavia dataset and the other a custom database of smartphone images in natural conditions. In  [35] , the authors used VGG and AlexNet CNNs for classification on the Plant Village dataset. This database comprises images of different diseases and species in laboratory and infield conditions. It is worth noting that the network trained on laboratory images does not seem to generalize well on real field images. Web images can also be gathered to create a plant disease database such as in  [36]. Using more advanced frameworks, Fuentes et al. performed the detection of tomato diseases using ResNet classification network and Faster R-CNN detection architecture  [37].
In that paper, a data augmentation process is used to generate more samples based on variations of existing samples and reduce the overfitting effect. Fruit detection is also tackled in  [38] , merging RGB and NIR Imagery, exhibiting good detection results even for partially occluded fruits. Sometimes authors also try to deeply modify existing methodologies, e. g. , in  [39] , the LeNet-5 network is used in combination with a novel k-means based weight initialization in order to improve classification performances of different weeds type in a soybean field and, in  [40] , a custom network is devised for the quantification of maize tassels on in-field images. CNN's state-of-the-art performances on many applications motivated the use of deep learning techniques for esca detection. From these works, several tendencies can be noticed: • Image databases tend to grow bigger and to be more diverse.
Data Collection and Processing In field data collection was performed during summer 2017, in mid-august. Two plots from the Bordeaux region of France were used at the basis for the experiment: red cultivar Cabernet-Sauvignon vineyard in Pauillac and white cultivar Sauvignon-Blanc vineyard in Castres-Gironde. In both cases, the 50 first plants were sampled on even numbered rows. Since esca plants represent about 5% of the plants, esca samples are more sparse than healthy samples, thus additional plants presenting esca symptoms were handpicked to complete the database. Cartography of esca prevalence in the last five years was available, allowing to know in advance which plants are diseased. Images were acquired using a RGB 2592×2048 camera; the camera was protected in a sturdy box packed with a microcomputer (image storage and acquisition program) and an electronic flash (Figure  3 ).
Data acquisition was then triggered using a custom smartphone app for one-time acquisition or regular acquisitions. The device was then mounted on a transformed wheelbarrow advancing in the vinerow and aiming at the plants on the right side (Figure  4 ). This device could also be easily mounted on a tractor for fast and automatic acquisitions. Lens and flash calibration depend on natural conditions and thus were done on the field to obtain a clear image of the grapevine plant foreground with homogeneous lighting and blurry background (next vine rows). A picture was taken for each plant, centered as much as possible on the trunk. Spatial resolution of images is about 1 mm. The differences in symptom expression shown in Figure  1  are greatly exacerbated by acquisition geometry and scene complexity.