dataesr/scientific-paragraphs-categorization huggingface dataset:
As a consequence, contaminant DNA from the reagents  42, 43  are more likely to have been amplified in those samples. Conversely, samples containing a high proportion of bacterial and archaeal DNA (fish gut or 0. 2-3 µm fraction sizes for example) are often free from contamination. Finally, the decontaminated amplicon sequences were again taxonomically assigned using SortMeRNA as described in Section 7. 4, which allows us to verify the efficiency of the decontamination process. acknowledgements Special thanks to the Tara Ocean Foundation, the R/V Tara crew, and the Tara Pacific Expedition Participants  44. This research was supported by a research grant from Scott Jordan and Gina Valdez, the De Botton for Marine Science, the Yeda-Sela center for Basic research, and the Sustainability and Energy Research Initiative (SAERI).
We are keen to thank the commitment of the following institutions for their financial and scientific support that made this unique Tara Pacific Expedition possible: CNRS, PSL, CSM, EPHE, Genoscope/CEA, Inserm, Université Côte d' Azur, ANR, agnès b. , UNESCO-IOC, the Veolia Foundation, the Prince Albert II de Monaco Foundation, Région Bretagne, Billerudkorsnas, Amerisource Bergen Company, Lorient Agglomération, Oceans by Disney, L'Oréal, Biotherm, France Collectivités, Fonds Français pour l'Environnement Mondial (FFEM), Etienne Bourgois, and the Tara Ocean Foundation teams. Tara Pacific would not exist without the continuous support of the participating institutes. This work was supported by the Genoscope, the Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA) and France Génomique (ANR-10-INBS-09-08).
As data inputs, the proposed approach used the existing data from the current speed profile, the load, and economic parameters. The desired output is the system component optimal sizing (TST power, and battery capacity). In this paper, the system sizing was studied during a one-year time period to ensure a more reliable and economical system. The results are compared to well-known methods such as genetic algorithms, particle swarm optimization, and software-based (HOMER) approaches. The optimization results confirm the efficiency of the proposed approach in sizing the system, which was simulated using real-world tidal velocity data from a specific deployment site.
HOMER (Hybrid Optimization Model for Electric Renewables) is the most widely used software-based technique for solving sizing problems and has been developed for both on-grid and off-grid systems. It was employed in  [60]  to size a hybrid system for rural areas in Saudi Arabia, Algeria, and Ethiopia. The HOGA (Hybrid Optimization by GA) software was used in  [61]  to enhance sensitivity in an autonomous system. These software-based methods are known for their user-friendliness  [62] , but they do not account for probability analysis or net measurements  [63]. Hybrid techniques combining different approaches have been used to enhance the sizing outcome. In  [64] , the authors combined simulated annealing and tabu search algorithms for an off-grid system to decrease computational time. In  [65] , a non-dominated sorting genetic algorithm (NSGA) and multi-objective particle swarm optimization (MOPSO) were adopted to simultaneously minimize carbon dioxide emissions and system cost.
The simulation results are compared to those from well-known methods such as genetic algorithms, particle swarm optimization, and software-based (HOMER) approaches to highlight the efficiency of the proposed method in improving reliability (with 0% DPSP) and reducing costs. This paper is structured as follows: the proposed system model and control are described in Sections 2. 1 and 2. 2, respectively. The energy management strategy is outlined in Section 2. 3. 3. The development of the sizing approach is described in Sections 2. 3. 2 and 2. 3. 3. Simulation results are analyzed in Section 3, and the paper concludes in Section 4.
To this end, a 47. 8 kW-800 Ah configuration was selected. With this configuration, the TNPC is USD 28,647, and the EC is USD 1. 164/kWh. 9  and Table  3  propose a comparison of the proposed approach to other techniques (genetic algorithm, particle swarm optimization, and HOMER software) at 0% DPSP. The achieved results clearly confirm the effectiveness of the proposed techno-economic optimization approach in obtaining a reliable and cost-effective TST-battery system. In this context, the particle swarm optimization technique achieves the second-best ranking, with a TNPC of USD 30,200 and an EC of 1. 296 USD/kWh. In contrast, the genetic algorithm technique is ranked the lowest, with a TNPC of USD 59,042 and a CE of 1. 761 USD/kWh. Figure  9  and Table  3  propose a comparison of the proposed approach to other techniques (genetic algorithm, particle swarm optimization, and HOMER software) at 0% DPSP.
Conclusions This paper presented an in-depth examination of an iterative optimal sizing method for a tidal stream turbine-battery system. The proposed approach used yearly tidal stream velocity and load demand as key input information for the optimization process. The results obtained from the application of this method clearly demonstrate its effectiveness in improving reliability (DPSP = 0%) and minimizing the overall cost of the system. The chosen configuration, at 47. 8kW-800Ah, was found to have the most favorable techno-economic performance, with TNPC and EC values of USD 28,647 and 1. 164 USD/kWh, respectively. The proposed approach has also been compared to well-established approaches, namely genetic algorithm, particle swarm optimization, and HOMER software. The achieved results clearly show that the iterative optimal sizing methodology allows obtaining a reliable and cost-effective TST-battery system.
The second is the FPGA resource occupation. It takes up about 47. 7% of resources, including 15. 8% registers and 32. 6% LUTs. The remaining resources can be used for deeper optimizations, such as loop Fig. 7. LabVIEW implementation. Fig. 7a  shows the behavioral description of the particle detection on the LabVIEW FPGA module and some available optimization. The subtraction, segmentation, and centroid calculation process occur in the pipeline by shift registers (2) in a time loop with an accelerated clock frequency (1). The particle coordinates are finally stored in FIFO and transferred to PC windows. Reducing FIFO quantity is another way to improve performance (3). LabVIEW Windows module (Fig. 7b ) handles these coordinates and returns the position of the particles in the real world. LabVIEW Windows benefits from multi-core execution to automatically run multiple independent processes in parallel.
Experimental evaluation We conducted a number of experiments to validate the efficiency of our generator RTGEN. We also provide an insight on how changing the input parameters can steer the characteristics of the generated temporal graphs. Note that the source code of RTGEN is publicly available  1. Besides the source code, we also provide the instructions describing how to use the tool to generate temporal graphs. For instance, users can pass the input parameters to describe the desired sequence of degree distributions or stochastic community matrix and the format of the generated output files to RTGEN using a terminal command. RTGEN proposes two output types: snapshotbased and event-based. The snapshot based type consists a sequence of graph snapshots represented each in a separate file.
Experimental setup The experiments were conducted on a single machine equipped with Intel(R) Core(TM) i5-8350U CPU @ 1. 70GHz 1. 90 GHz, 16 GB memory and 500 GB SSD. We used Go 1. 17. 5 and Python 3. 8. 0. Besides, we referred to the optimal transport solver proposed in  [25]. The graphs shown in this section are visualized using Gephi tool  [26]  which offers network visualization facilities and community detection algorithms  [27].
This paper presents the work produced by students of the University of Orléans Masters in Natural Language Processing program by way of participating in SemEval Task 6, LegalEval, which aims to enhance the capabilities of legal professionals through automated systems. Two out of the three sub-tasks available -Rhetorical Role prediction (RR) and Legal Named Entity Recognition (L-NER)were tackled, with the express intent of developing lightweight and interpretable systems. For the L-NER sub-task, a CRF model was trained, augmented with post-processing rules for some named entity types. A macro F β=1 score of 0. 74 was obtained on the DEV set, and 0. 64 on the evaluation set. As for the RR sub-task, two sentence classification systems were built: one based on the Bag-of-Words technique with L-NER system output integrated, the other using a sentence-transformer approach.
Introduction The ever-increasing volume of legal documents in populous countries such as India, for example, shows that automated systems may prove to be highly beneficial. Automating such processes reduces the need to perform unrewarding tasks such as manual information retrieval, thereby facilitating the work of legal professionals, while reserving more complex aspects of law and decisions for their intervention ; and shortens the overall time required for legal processing  (Kalamkar et al. , 2022b). Two out of the three LegalEval 2023  (Modi et al. , 2023)  sub-tasks available were tackled: the Rhetorical Roles (RR) textual classification task (sub-task A) and the Legal-Named Entities Extraction (L-NER) task  (sub-task B). Systems for the sub-tasks were not developed in silo: some RR systems made use of data that was first pre-processed with L-NER system-generated annotations, which in some cases improved performance on the DEV set by approximately 2 points 1.
Explainability is especially characteristic of CRFs, while more complicated systems such as Deep Learning approaches that employ standard real-valued tensors (i. e. , x ∈ R d 1 ×d 2 ×. ×dn ) are not as interpretable. The authors' participation was an attempt to test the capabilities of lightweight systems in achieving reasonable performance on specialized data, given a lack of no prior knowledge or competencies in the legal domain that could provide leverage in tailoring systems for legal processing. The code is available in a GitHub repository (https://github. com/LouisEsteve/LegalEval2023). The rest of the paper is structured as follows: (1) Presentation of the two sub-tasks (Section 2); (2) Systems overview in (Section 3); (3) Experimental setup (Section 4); (4) Results and error analysis (Section 5); (5) Conclusion and way forward (Section 6).
L-NER The L-NER sub-task aims to perform Named Entity Recognition in the legal domain, a practical application being, for example, the automated retrieval of relevant information. To do so, thirteen classes were to be annotated (details in Table  1 ). Of note are five classes relating to people, which will be relevant for analysis in later sections. The datasets for this sub-task, as shown in Table  2 , are themselves split into preamble and judgement; both were used interchangeably to train and estimate systems' performance. It should also be noted that the preamble seems to contain significantly more entities relative to its number of sentences than the judgement. Grishman and Sundheim (1996)  were the first to discuss "named entity recognition" (NER) in the context of the MUC conferences.
RR The RR prediction sub-task deals with text classification in Indian court judgements written in English by legal professionals and often of a substantial length (average of 119 sentences/document in the TRAIN dataset). This sub-task differs from the traditional text classification task of assigning one label to an entire document. Instead, the document is to be decomposed into rhetorical roles (RRs) and a class is to be assigned to each RR found. 12 RR classes have been identified (Table  3   2  ), some of which pertain specifically to legal documents, e. g. RLC (Ruling by Lower Court), ARG PETITIONER. Another label has been added in order to classify sentences that do not belong to any of the 12 RR classes: NONE. Each sentence must be labelled with one of the 13 classes (12 informative and NONE).
Some statistics characterizing the datasets provided by the organizers can be found in Table  4. This task has been previously explored and tested on different corpora with different sets of Rhetorical Roles. Hachey and Grover (2006)  worked on the summarization of legal texts based on the classification of sentences into 7 RR classes (fact, proceedings, background, etc. ). They tested 5 classifiers and obtained the best performances with SVM and maximum entropy sequence models. Saravanan and Ravindran (2010)   3 System overview.
L-NER With an objective of reasonable interpretability, a standard CRF  (Lafferty et al. , 2001)  was chosen for the L-NER task. The Python library sklearn-crfsuite 3 was used. The overall structure is as follows: 1. Process the texts with SpaCy  (Honnibal et al. , 2020)  and retrieve token-level features 2. For each token of each text, add as features those of previous and upcoming tokens on an -m : +n span, such as -2:head for the syntactic head of the token two positions ago, or +1:morph for the morphological aspects of the token one position ahead 3. Feed this structure to a CRF 4. Apply the CRF model on the TEST set 5. Optionally, apply post-processing (regular expressions to extract dates, case numbers and geopolitical entity) rhetorical-role-baseline 3 https://pypi. org/project/sklearn-crfsuite/ The regular expressions for the post-processing step were constructed using the TRAIN set for reference.
For DATE, a variety of formats were included, such as digit-based (e. g. , DD-MM-YYYY, MM-DD-YYYY), or equivalents with months written in letters. For CASE NUMBER patterns, this included specific indicators (e. g. , "civil", "criminal", "appeal") adjacent to the token being processed. For GPE, this consisted of a very basic (and naive) pattern: a token with at least three capital letters. Several instances of tokens following this format were observed, however, deploying this regular expression proved to be detrimental. For further information with regards to the Python version, module versions, and SpaCy model used, please refer to the GitHub repository (https://github. com/LouisEsteve/LegalEval2023).
[Bag-of-Words] As for the Bag-of-Words system, the model was developed using the scikit-learn library  (Pedregosa et al. , 2011). Our findings indicate that the linear regression algorithm outperformed other algorithms available. To achieve this result, we used a combination of TF-IDF weights and tested out uni-and bi-grams. [sentence-transformers] For the vectorial approach, each datapoint was converted into a sentence vector using the SBERT sentence transformer 5   (Reimers and Gurevych, 2019). The transformed data was then fed to a K-Neighbours model. The input test dataset was also converted into a sentence vector per datapoint for the model to classify into the 13 classes. Both of these approaches classify sentences independently into one of the 13 classes. In order to move from sentence classification to text segmentation, we added a rule-based post-processing phase.
RR [Bag-of-Words] A five-fold cross-validation technique was employed, with a grid search to optimize the hyperparameters summarized in Table  6. The system was tested with and without preprocessing (the replacement of named entities detected by the L-NER system with their type). The sentencetransformers 7 architecture and pretrained sentence embedding model all-MiniLM-L6-v2  (Reimers and Gurevych, 2019)   8 were employed as-is to generate sentence embeddings for both 6 Stands for "Passive Agressive" according to sklearn-crfsuite's documentation  (Crammer et al. , 2006). 7 https://www. sbert. net/index. html 8 https://huggingface. co/nreimers/ MiniLM-L6-H384-uncased datasets without any fine-tuning due to material constraints. A scikit-learn 9  K-Neighbours classifier was then trained on the TRAIN dataset, and later employed to generate predictions on the DEV dataset.
74). Having no access to the TEST set at the time of writing, it is not possible to accurately assess the reasons behind such a difference in performance. Under such circumstances, the default hypothesis (1) CASE_NUMBER (2) COURT (3) DATE (4) GPE (  5 ) JUDGE (  6 ) LAWYER (  7 ) ORG (  8 ) OTHER_PERSON (  9 ) PETITIONER (  10 ) PRECEDENT (  11 ) PROVISION (  12 ) RESPONDENT (  13 ) STATUTE (  14 ) WITNESS System prediction would be that our system could not generalize well enough for its performance to be consistent over different data sets. (1) (2) (3) (4) (5) (6) (7) (8) (9) Regarding errors, analysis of the version trained on TRAIN and tested on DEV shows that the system's main issue lies between RESPONDENT and PETITIONER; overall, the frontiers match the reference but the two classes tend to be mistakenly annotated as the other.
A more exhaustive confusion matrix is available in Figure  2. A meta-observation made is that it is entities of the general type of people that causes difficulties for the system, implying that differentiating the specific roles of people goes beyond the CRF system's capabilities. The effect of the post-processing phase can be observed from row 2 and onwards in Table  8. DATE, CASE NUMBER, and GPE were all postprocessed using regular expressions based on what we could extract from the TRAIN set. It seems that basing regular expressions on limited data does not ensure high quality, as the differences in score suggest. Even for DATE that was thought to have reasonably stable patterns (e. g. ISO-8601), ground truth indicates that strict patterns do not seem to be a reliable occurrence.
Model Macro F1 As shown in Table  9 , the best results are obtained by the Bag-of-Words system with pre-and post-processing phases. Substitution of the entities detected by the L-NER system improves the performance of the Bag-of-Words system by two points, but does not impact the results of the sentencetransformers system (slight increase in precision). There is a significant increase in the macro F β=1 metric from the DEV to TEST results. One hypothesis is that the two datasets are rather varied but the systems are able to generalize across the two datasets, performing even better on the TEST dataset. On the TEST set, the Bag-of-Words system that utilized pre-annotated data from the L-NER task achieved the best results (Macro F β=1 = 0. 568). The other approach trialed, sentence-transformers, followed close behind (Macro F β=1 = 0.
551). The sentencetransformers method was also tested with the preprocessing lemmatisation step, obtaining poorer results (Macro F β=1 = 0. 529) than without. The system that ranked first at the LegalEval 2023 shared task, developed by the AntContentTech team, obtained a Macro F β=1 of 0. 859. It is based on BiLSTM and CRF, and employed the LegalBERT model. An error analysis of the systems tested on the DEV dataset showed that unsurprisingly, the best-represented classes (ANALYSIS, FAC and PREAMBLE making up 2,076 out of the 2,890 sentences) tended to perform the best across all 3 metrics (precision, recall and F1) among the 13 classes, while the least-represented classes (PRE NOT RELIED) performed the worst in all 3 metrics or obtained very lopsided results (1. 0 in precision and 0. 0 in recall and F1).
Conclusion Our participation in the LegalEval task was motivated by the express aim of evaluating the competitiveness of lightweight systems, with the interpretability of the system as a bonus criterion. Results obtained in the RR and L-NER tasks were modest, at 0. 568 and 0. 649 and placing 25th (out of 27) and 14th (out of 17) respectively. For the L-NER task, we found that (1) CRFs may attain a reasonable performance level (Macro F β=1 ≈ 0. 74 on DEV) when trained to annotate all the classes at once, but (2) they face issues when having to differentiate between very similar classes (e. g. , between PETITIONER and RESPONDENT) and (3) it is not certain if they are really capable of generalizing across datasets (Macro F β=1 ≈ 0. 64 on TEST, 10 points below DEV). For the RR task, we found that replacing named entity spans with their class slightly improves performance of RR systems to a certain extent.
, saturation after leaving a tunnel while driving), and a lower latency than regular cameras, which makes them particularly suitable for challenging, highly dynamic tasks (event sensors do not suffer from motion blur, unlike their framebased counterparts). Nevertheless, they can also be less expressive: event cameras only provide information regarding changes in luminance, and not about the luminance itself. Furthermore, most event cameras discard color information, although some devices exist with independent firing for RGB formation at each pixel, like the Color-DAVIS346 event camera used by  Scheerlinck et al. (2019)  to generate their CED Dataset. Finally, event cameras usually have lower spatial resolution than regular cameras, although recent technological developments are bridging this gap (e.
Related work Ever since their introduction, event cameras have been gaining ground within the computer vision community, and increasing efforts have been made to develop computer algorithms based on event data. As such, different datasets have emerged in order to solve different kinds of computer vision problems, like the DVS128 Gesture Dataset by  Amir et al. (2017)  for gesture classification, or the EVIMO Dataset by  Burner et al. (2022)  for motion segmentation and egomotion estimation. Despite this interest in event vision, the significant investment that event cameras represent for most research centers and companies has led to the development of event data simulators such as CARLA by  Dosovitskiy et al. (2017) , as well as algorithms to perform video-toevents conversion, like the model proposed in  Gehrig et al.
(2021b). While lacking the intrinsic noise event data usually presents, these artificial data can nonetheless be used to efficiently pre-train computer vision neural networks, e. g. ,  Hidalgo-Carrió et al. (2020)  pretraining their model for depth estimation on a synthetic set of event data. Nonetheless, for real-world applications (e. g. , gesture recognition, object detection, clustering, etc. ), true event recordings are preferred because simulators are still lacking realistic event noise models. Concerning depth and/or optical flow regression, two datasets have currently established themselves as the go-to choices: the MVSEC Dataset by  Zhu et al. (2018a) , and the DSEC Dataset by  Gehrig et al. (2021a). While all of these datasets have proven invaluable to develop event-based computer vision algorithms, there is still an enormous gap between event-based and image-based publicly available datasets, and many authors are still forced to develop their own.
For example,  Cordone et al. (2022)  generated their own classification data from de Tournemire et al. (2020) to account for the additional "pedestrian" class. Most models so far have either been standard Analog Neural Networks (ANNs) like  Gehrig et al. (2021b) , exploiting gatedrecurrent units to achieve state-of the art accuracy on DSEC, or hybrid analog-spiking neural networks like  Lee et al. (2022) , combining a spiking encoder with an additional analog encoder for grayscale images, followed by a standard ANN. Other models have tried to leverage the temporal context by feeding the network with not only the events themselves, but also information on event timestamps, like the EVFlowNet model presented in  Zhu et al. (2018b). More recently,  Zhang et al. (2022)  showed temporal information to be a key in accurately estimating both optical flow and depth, achieving top results in the MVSEC and the DSEC datasets thanks to their implementation of non-spiking leaky integrators with learnable per-channel time constants.