dataesr/scientific-paragraphs-categorization huggingface dataset:
Scaled sketches As the problem of inaccuracy of fixed-size sketches for dissimilar datasets was pointed out but only partially solved using MinHash schemes [27, 28], novel approaches were proposed. Scaled sketches methods build their sketches with adapted sizes in regard to the input. The main contribution in this direction for compositional analysis is sourmash [14], and our paper also falls in this category. sourmash incorporates a scaled factor indicating the fraction of the k-mer's input that should be included in the sketch as fingerprints. To do so they add a fingerprint in the sketch if their integer value is a multiple of S (ScaledMinhash) or lower than a fixed threshold (FracMinHash). The latter technique is recently described and theoretically studied in  [15, 29]. In contrast to previously presented fixed-size sketching approaches, those approaches have the advantage of providing a more accurate comparison of uneven sized sets.
Second, this technique enables a more spaceefficient representation of k-mers, typically requiring less than the 32-bit per k-mer space cost of sourmash. Figure  2  illustrates an example of sketch construction in SuperSampler. In the following sections, we propose a model to evaluate the space efficiency of a SuperSampler sketch. In the regular case with hashed minimizer with a density factor of 2 we can expect (k -m + 1)/2 k-mers per super-k-mers  [22]. This results in a mean super-k-mer length of (3k -m -1)/2 bases. We can give a lower bound of the cost in bit per k-mer to encode such super-k-mers: 2(3k -m -1) k -m + 1 However we need to encode the length of each super-k-mer to avoid considering artefactual k-mers created by two successive super-k-mers so we can add log 2 (k -m + 1) bits per super-k-mer (encoding the number of k-mers).
Performance Comparison In our qualitative experiment, we compared the performance of SuperSampler with sourmash, which implements FracMinHash. We evaluated both tools on two distinct datasets: 1024 Salmonella genomes from GenBank and 1024 bacterial genomes from RefSeq. These collections were chosen due to their differing containment indexes; Salmonella genomes are highly similar to each other, while the bacterial genomes in RefSeq exhibit much greater dissimilarity (Jaccard similarity close to 0). We carried out an all-versus-all comparison of these collections using both tools and monitored RAM and disk usage, as well as computation time during the sketch comparisons. To assess the precision of the approximated scores, we calculated the exact Jaccard and containment similarity values using Simka  [35] , which performs efficient k-mer counting operations on large collections.
With these scores as a reference, the precision of the approximation can be evaluated. RAM and computation time were measured using the benchmark flag from Snakemake, with one run per command. Disk usage was determined by comparing the sketch sizes of sourmash (using the zip option for sourmash sketch) and SuperSampler, with the latter's sketch sizes examined through a Python script. SuperSampler sketches were stored in a tar archive and compressed using gzip -9.
Funding This study was supported by a grant from the  French National Research Agency :  AGATE ANR-21-CE45-0012. Co-funded by the  European Union  (ERC,  IndexThe-Planet ,  101088572 ). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the  European Research Council. Neither the  European Union  nor the granting authority can be held responsible for them.
g. [7] , with spatial variations of the coefficients in the covariance model  [18]. Beauchamp et al. [3]  also developed a cokriging model for mapping PM 10 and PM 2. 5 , in order to improve the precision of PM mapping. In this work, the local means of both variables are related by an additive model, but the latter does not ensure to satisfy the physical relationship existing between the two after their joint estimation. Indeed, the PM 2. 5 concentration is by definition less than PM 10. At most, the additive model of Beauchamp et al. [3]  ensures the drift, a local regression of the observations on the Chemistry Transport Model (CTM) outputs  [24] , to be physically consistent thanks to the appropriate unbiasedness conditions. Thus, when the correlation between the model and the two observational datasets is good enough, the final estimation is very likely physically consistent.
Results In this Section, we proposed an application based on the dataset used in Beauchamp et al. [3]. The specific additive modelling introduced in the above-mentioned work is used to compare both mappings and performance of the penalized cokriging (PCK) algorithm with the usual way of solving the cokriging system (CK). The background PM 10 and background PM 2. 5 observational dataset is then identical to Beauchamp et al. [3] , but extended from France to Europe, see  ]), with a coarse resolution of 0. 5 •. The maps are interpolated by cokriging on a regular grid with the same resolution used by CHIMERE for solving the chemical and physical processes. The time period covers the first quarter of 2015 in which the PM concentrations were particularly high with a long episode of pollution occuring in March 2015.
Regarding the practical implementation, an original C++ program has been developed to run the penalized cokriging version. The armadillo C++ library  [12]  is involved for the numerical analysis related to kriging. An interface with R software  [29]  is used inside the C++ program towards the R library RCppArmadillo  [17]  so that all the descriptive geostatistical part, e. g. the variogram computation and fitting, are done by RGeostats through this interface. The full code is available via Zenodo (10. 5281/zenodo. 7756425). It is ready-to-use after installation of the appropriate libraries and specifications of the input data. The appropriate shape for the inputs is given as comment lines along the program.
When the inequality constraint is not satisfied and the iterative penalized scheme is involved, less than 10 iterations were generally involved in our datasets (i. e. less than 10 observational weights are non-basic) and no estimation location ended with a simple heuristic interpolation (when the number of iteration reaches the number of observations, thus excluding most of the available information). This specific cases happened when PM 2. 5 and PM 10 observations are both close in terms of values and spatial locations.
For example Veronika Irvine  (Irvine & Ruskey, 2014)  proposed a tessellation-based approach that characterises repeated elements on a pattern. The pattern can then be simulated using parametric mathematical modelling based on graph theory. This simulation allows the creation of a software tool called Tesselace  (Irvine, 2019)  which automatically generates pair and thread diagrams for 2D pattern representation. This software led to the development of the collaborative tool Groundforge  (Pol & Tempels, 2020) , which brings together lacemakers from around the world to create a tool suitable for designing new lace patterns. Several recent studies have shown that the use of digital technologies such as Computer-Aided Design (CAD), 3D printing and 3D scanning can be combined with lace in innovative projects, as demonstrated by the work of Lisa Marks  (Marks, 2019).
Similarly, these digital tools can also be combined with industrial lace as demonstrated in Ma(T)isse breast reconstruction project (Ma(T)isse-project, 2018). These studies suggest the possibility of experimenting with the creation of 2D laces designed using parametric methods. Our objective is to take this concept further by proposing the design and manufacture of a 3D pattern that serves as a foundation for the lacemaker to produce their work. The lace is thus made directly on a three-dimensional pattern, requiring a rethinking of the traditional techniques and gestures involved in its production. This article explores the potential for designing and producing a three-dimensional lace using innovative technology to extend its range of applications. The proposed method involves the use of LaceApp software to create a 3D pattern.
Lace-App aims to convert 3D meshes into a 3D model with lace texture. A skeletonisation technique using Reeb graphs is used to segment the mesh  (Edelsbrunner & Harer, 2010). The Tesselace model is then propagated from a starting point of the 3D shape along the width of the mesh (Fig. 4 ). This process is implemented using Python 3. 8 to generate graphs of lace patterns with their corresponding textures. These graphs are developed using the Networkx library and are then converted to lace textures using Python's native library for image processing, PIL. A function has been added to track the path of the lace threads segment by segment. It makes it possible to check the consistency of the technical model with the help of GIF. The LaceApp offers various customisation options, such as changing the aesthetics of the pattern, selecting the lace points, changing the colour and scale of the threads and more.
3D Meshed Model The use of the LaceApp software application allows the modelling of the thread paths directly on the 3D shape and the precise determination of the pin placement. Figure  5  shows a numerical thread path on the selected shape with different colours indicating the different pin positions. The tool allows the pattern to be scaled to fit the shape of the object and the threads to be coloured for easy visualisation during the sampling process. Experiments have shown that 92% of the holes are accurate. This accuracy makes the process of lacemaking on the three-dimensional object efficient.
Conclusions The research aims to demonstrate the effectiveness of incorporating digital tools into the production process of handmade bobbin lace. The result is the production of threedimensional lace while maintaining traditional techniques and gestures. The study confirms that the use of digital tools facilitates the design of the technical drawing, the pattern and thus the production of the lace. The ability to visualise the arrangement of the threads in a three-dimensional space through direct virtual simulation on the object saves time in the design process. Drawing the lace digitally with software such as Lace-App creates a precise and coherent pattern that optimises the consumption of threads needed to produce the desired item. Immersing a flax piece in water to dissolve the "carton" did not cause any deformation, deterioration of the colour, or affect the appearance of the object.
The authors solution uses named entity recognition and detection rules. The first phase which consists of named entity recognition uses a tool to automatically annotate the entities in textual data. The second phase which is the detection of the type of event uses the business rules set up from the ontology. Piskorski et al. (2007)  with the goal of populating a knowledge base on violent incidents, propose an automatic event extraction system "Nexus" based on a mix of machine learning and ontology-based approaches. "Nexus" extracts security-related facts from news articles. This system is composed of 3 modules: (i) the Europe Media Monitor (EMM) module that deals with retrieving news articles, (ii) a second module that deals with pre-processing the texts to make them more abstract. It implies the use of an NER and a clustering algorithm to group the entities in cluster.
(3) Hybrid approach: this approach uses both previous approaches for event extraction. It is interesting because it allows to obtain good performances during extraction while using less data than the databased approach and less expertise than the purely knowledge-based approach. We propose here to follow the hybrid approach, combining the expertise of maritime transport actors with recent advances in NLP and in particular with the BERT method than enables to encode in such a way as to transcribe the semantics and context of a word. We will start by setting the scene of maritime transport with a description of the data to which we have access in the first place and how they serve as a starting point to reach information likely to influence the course of a given shipment.
From IoT data inherent to the container to exogenous information Tracker devices are mounted on the door edge of containers. They regularly sends frames of data through the network to reach and feed a database that is following the model in Fig 2. At the core of the latter, there is the table event which receives notifications from all devices. This event may be for example a crane operation, a door opening, some random movement. They are timestamped and located. All those event occurrences intersperse and provide information on the container path and enables to build numerically the whole shipment described schematically in (Fig  1 ). The latter takes shape in the base as the table shipment. It comprehends overall shipment metadata. For an event example: A door opening has been detected on a container tracked by device identified by device serial XXX in the port of Durban at 29°53'6.
197 South 31°2'40. 489 East on Friday the 14th April 2022 at 09:26:52 UTC. The event table is used to construct the segment and shipment tables. These tables give their coherence to the series of coordinates points. Though the focus is on events. Even more specifically an exhaustive typology of the event populating the base is not indispensable as what serves the service proposed here is the transmitted GPS coordinates with related timestamp. From these events, which are inherent to the container since they are attached to it, it is about reaching information that informs higher level circumstances that could reshape its global route. The tracker notifies the occurrence of an event that reaches the database. This event restricted to container level is characterized by numerous data and especially by its longitude and latitude coordinates and a timestamp.
In this scenario, the main challenge is to develop real-time VVC codecs, either a hardware or software solution for video encoding or decoding, that consider resource-constrained consumer devices frequently used in consumer electronics based on embedded platforms. Each coding standard is released with a reference software implementation available to the scientific community. These solutions incorporate all the standard features but offer minimal speed performance. For example, in the case of VVC, the reference software is VVC test model (VTM)  [4]. Taking this as a starting point, research groups and companies develop their own real-time software and hardware solutions. These solutions mainly exploit the intrinsic parallelism of the algorithms, both at the data and functional levels, to enhance their performance in terms of speed and energy consumption.
In the first case, some data operations included in the source code are optimized using instructions of type single instruction multiple data (SIMD)  [5]. Here, vectorized operations perform mathematical operations with more than one operator using a single processor instruction. The other potential optimization route is to take advantage of the intrinsic parallelism of the independent processing of pictures  [6]  or smaller parts of the picture, such as slices  [7]  or tiles  [8]. In the latter case, it is necessary that the coding is done by activating these normative tools that break dependencies between adjacent regions. In this work, two open-source VVC decoders are evaluated and compared against each other. These solutions, named OpenVVC  [9, 10]  and Versatile Video deCoder (VVdeC)  [11]  decoders, are optimized using data and functional-level parallelism techniques.
The authors of  [23]  presented a GPU-based motion compensation system to accelerate the VVC decoder that takes advantage of partitioning different coding unit (CU) threads and proper organization. Furthermore, Wieckowski et al. in  [24]  described an optimized VVC decoder that achieved real-time decoding on generalpurpose CPUs. Here, SIMD operations-based optimization and multithreading-based optimization were adopted. The authors in  [25]  demonstrated an optimized real-time VVC decoder that takes advantage of SIMD instruction extensions on x86-based CPU. Moreover, the authors discussed the implementation of the frame, CTU, sub-CTU, and tasklevel parallelizations. An optimized VVC software decoder for mobile platforms is presented in  [26]. This decoder is based on VTM -11. 0 reference software, and achieves real- time decoding for HD video sequences using SIMD and multi-threading on ARM  [19]  based platform.
Open-source VVC decoders A few open-source software decoders are available that comply with the VVC standard. First, the reference test model VVC mentioned above, or VTM. Second, VVdeC [11], an implementation proposed by the Fraunhofer Institute, is an optimized decoder based on VTM. It includes SIMD and multi-threads parallelization for optimal decoding performance. Finally, OpenVVC is a lightweight open-source software decoder available in  [9]. OpenVVC decoder targets different operating systems and hardware architectures. Similarly to VVdeC, OpenVVC uses data and functional-level parallelism to optimize the decoding performance. For more details on VVC codecs, Sullivan  [28]  provides a complete list of available VVC encoder and decoder implementations.
Introduction to OpenVVC OpenVVC is an open-source software VVC decoder written in C programming language. It is compiled as a crossplatform library, compatible with most-used operating systems, and optimized for x86 and ARM processors. The last version of the decoder is compatible with the VVC Main profile. In addition, the decoder was integrated with VLC  [29] , GPAC  [30] , and FFplay  [31]  video players. OpenVVC provides high decoding speed with a low memory footprint. It takes advantage of tile-and frame-based parallelization on multi-core CPU along with SIMD optimizations for accelerating the decoding process. The OpenVVC decoding process starts by parsing the sequence parameters. The reconstruction tasks, including inverse quantization and transform (TX), LMCS, inter-prediction (EP), and intra-prediction (IP) decoding blocks, are performed at the CU level.
Decoder optimizations In previous work  [34] , VVdeC V0. 2 was optimized for ARM architectures using Neon-based SIMD parallelisms. The source code is openly available in  [35]. Moreover, the optimization of VVdeC V1. 3 is similar. Therefore, this section mainly focuses on implementing frames, tiles, and Neonbased SIMD parallelisms in OpenVVC over EGPP-based platforms.
Prediction dependencies across tile boundaries are broken and entropy encoding state is reinitialized for each tile. These restrictions ensure that tiles are independently decoded, allowing several threads to decode simultaneously the same picture. As it can be observed, regardless of the tile position, as soon as a thread is available from the thread pool, Fig. 3  Illustration of tile pipelining in OpenVVC decoder the tile is processed. Thread 2, for example, does not work on any tiles of the second frame since it takes the entire time working on tile D of the first frame. This fact does not restraint threads 0 and 1 from working on the tiles of the second frame. However, adopting this technique creates a type of combination between frame and tile parallelism, as a result, dependencies between frames for inter-prediction and motion compensation should be taken into account.
This fact allows concurrent data processing to achieve a theoretical speedup of up to ×16 on 8-bit data. In this study, Neon-based SIMD instruc- tions are used to optimize the high computational demanding VVC decoder modules presented in Table  1  by adapting the SIMD  [36]  library. Here, DST-VII, DCT-II, DCT-VIII, inter-component transform and low-frequency non-separable transform (LFNST) module of TX block of VVC decoder was accelerated using SIMD registers. TX involves several matrix operations including matrix multiplication for the inverse transformations. These matrix operations were tackled using SIMD intrinsics based on logical and mathematical operations: vand, veor, vadd, and vmul. The most benefiting modules of EP block by SIMD parallelization are luma 8-tap filters, chroma 4-tap filters, bi-directional optical flow, decoder side motion vector refinement, and prediction refinement with optical flow.
Test video sequences Table  2  presents the different features of the 15 JVET common test sequences  [39]  used in this study. The following sequences, grouped by resolution classes, have been encoded by the VTM -11. 0 reference software with 10-bit random access and 4 × 3 tile configuration at two QP 27 and 37. As it can be seen, only A-and B-class sequences have been included. Lower resolution class sequences were discarded as in most cases the performance obtained with them would be above the real time, following the trends proportional to the results observed with the chosen test set.
Energy consumption Energy consumption is another important factor for video processing operation over embedded platforms. The characterization of the impact of the software on hardware is essential to obtain models that allow the identification of the optimal working points of video decoders  [41]. In this study, the energy consumption was calculated as follows: 1) the power consumption is taken (in mW) after decoding each frame using the built-in power monitor of both ESoC, 2) the average power consumption of the entire sequence is multiplied by the total time in seconds spent decoding the sequence. The average energy consumption in J with different configurations of OpenVVC and VVdeC decoders is shown in Fig. 8. Here, OpenVVC and VVdeC have consumed comparable average energy in ESoC1 for all configurations.
Comparison between OpenVVC and VVdeC decoders Both open-source optimized video decoders OpenVVC and VVdeC have reached real-time for FHD and HD sequences over ESoC1 using 8 cores. In addition, both solutions present results close to real-time performance for UHD sequences on ESoC1 platform. Furthermore, the OpenVVC and VVdeC decoders achieved an average of 22 fps for QP27 and 28. 5 fps for QP37 using 8 cores. Tables  5  and 6  show the average performance (in fps) of OpenVVC and VVdeC using different number of threads on ESoC1 and ESoC2. OpenVVC introduces slightly more runtime complexity compared to VVdeC: 3% for UHD, 5% for FHD and 12% for HD sequences in both platforms. To summarize, there are three important parameters to take into consideration to select a video decoder for an embedded platform with limited hardware resources: the performance (fps), the energy consumed to decode a video, and the memory used.
The performance of the decoders compared in this paper (VVdeC and OpenVVC) is very similar and only a small improvement is achieved in VVdeC while the number of cores remains low. The energy consumed to decode a sequence is also very similar between both decodes. Finally, OpenVVC consumes less memory than VVdeC with a factor greater than ×2. 11. This sig- nificant reduction makes the OpenVVC decoder a suitable option to implement a VVC conformant video decoder in a multi-core platform with limited resources.
In an industrial context, faults are rare relative to normal weld cycles. Thus, an appropriate method for detecting such defects would originate from the time series outlier detection domain, which is a research subject that recently gained a lot of interest due to its numerous applications across various fields, such as fault detection in manufacturing, intrusion detection and cybersecurity. Many real-world problems focus on finding outliers from time series datasets (  [1] ). An outlier (or an anomaly) can be defined as a pattern that does not conform to a well-defined notion of normality (  [2] ). There are three types of time series anomaly detection; point anomaly, subsequences anomaly and full-series anomaly. In our work, we are interested in the detection of abnormal subsequences, also known as collective anomaly, which are defined as consecutive points in time that have unusual behaviour.