dataesr/scientific-paragraphs-categorization huggingface dataset:
Starting from 0%, 10%, up to 100%, we obtain the performance of the model for different thresholds. From these scores, the faithfulness is then measured by computing the area under the threshold-performance curve (AUC-TP). Dataset Consistency (DC) DC measures if an explanation is consistent across instances of a dataset. Two instances similar in their features should receive similar explanations. Similarity between instances is obtained by comparing their activation maps, and similarity between explanations is the difference between their saliency scores. The consistency score is then the Spearman's correlation ρ between the two similarity scores. The overall dataset consistency is the average obtained for all the sampled instance pairs.
Hyperpartisan (HP) A binary classification task  (Kiesel et al. , 2019)  of predicting whether a given news article is hyperpartisan or not (takes an extreme left-wing or right-wing standpoint), task 4 of SemEval-2019. We considered the dataset containing 1, 273 manually annotated articles. C-POLITICS We built on the large-scale news articles dataset POLITICS 7   (Liu et al. , 2022). It comes with an aligned version containing 1, 060, 512 clusters of articles aligned on the same story from 11 media. We propose a reduced version of this dataset meeting three desirable constraints: class balance, temporal framing and media-agnostic. We kept only articles published between 2020 and 2021 (annotation stability), excluding the possibility of a media appearing in several splits (train, validation, test) and forcing to have at least one article of each label per cluster (homogeneity).
Experimental Settings Baselines For Allsides and Hyperpartisan, we compare to the results obtained by the authors of the datasets, and the winners of the task (HP). We also compare to three additional transformerbased baselines on the three tasks, for which we fine-tuned a classification model (on a single run): (1) RoBERTa-base  (Liu et al. , 2019b)  (2) Longformer-4096  (Beltagy et al. , 2020) , a language model designed to handle very long sequences of text, up to 4096 tokens (3) POLITICS  (Liu et al. , 2022) , a state-of-the-art language model built over RoBERTa-base for political ideology prediction, pretrained on more than 3. 6M news articles (see above). RoBERTa and POLITICS are fine-tuned on the whole input using a sliding window of size 512 and an overlap of size 64; we built on Liu et al.
(  2022 )'s implementation 8. All baselines and proposed models have similar numbers of parameters (cf. the appendix). For the explanations, we compare to the original version of LIME for text classification, which is based on words perturbation, and a random explanation on the whole input. Settings For the classification model, we built on Ferracane et al. (  2019 )'s implementation, 9  itself based on Liu and Lapata (2018)'s. We adapted the code according to the modifications and additions proposed in our approach, as detailed in Section 3. 1. Hyperparameters were set using grid search and are the same for all tasks (Table  8  in Appendix B). We used pretrained 300D GloVe vectors  (Pennington et al. , 2014). For the AA training, since the training set may contain many media sources with a long tail distribution, we only consider the 10 most frequent sources.
Hyperparameters for the finetuning of RoBERTa, POLITICS and Longformer are given in Appendix B. 2-level explanations are generated using the 10 most impactful EDUs. Evaluation We evaluate two versions of the classification model: segmentation into sentences, or into EDUs (on a single run). We report accuracy as it is the standard measure in previous work on these tasks. We built on the LIME python package 10  to implement our methods (Section 4). We generate and evaluate explanations on 100 documents from the test set for 1, 000 and 10, 000 perturbed samples and compute a score for each feature. Explanations are generated for our trained classification model with EDU segmentation (Section 3. 1). The confidence interval for the evaluation of the explanations is only given for the baseline (LIME Words) for 10 generations.
Limitations We reused data collected by previous work in the literature. Collecting news articles is susceptible to various sampling biases, related to the sources collected, the topics covered, and the time span of the collection, which influences what appears in the articles. In addition, labels given to articles are actually the political orientation of their source in the case of the Allsides and POLITICS datasets, which is obviously likely to induce errors. They rely on expertise provided respectively by the Allsides 11  and Ad Fontes 12  websites. The exact methods are undisclosed, but such labeling has necessarily a subjective aspect, oversimplifying predefined political categories, and can evolve in time. This affects classification reliability when applied to different sources, different times, different topics.
Acknowledgements Nicolas Devatine 's work is supported by the  SLANT  project ( ANR-19-CE23-0022 ). This work was partially supported by the  ANR  ( ANR-19-PI3A-0004 ) through the  AI Interdisciplinary Institute ,  ANITI , as a part of  France's "Investing for the Future -PIA3" program. This work is also partially supported by the  AnDiaMO  project ( ANR-21-CE23-0020 ). Chloé Braud  and  Philippe Muller  are part of the programme DesCartes and are also supported by the  National Research Foundation, Prime Minister's Office, Singapore  under its  Campus for Research Excellence and Technological Enterprise (CREATE)   programme. We thank  Farah Benamara  for her helpful comments and suggestions on an earlier version of the paper.
Firm-level quarterly financial data related to Cz + lkt , Cz - lkt , and Cy mkt from  (11)  were collected in the Economática database. These companies were publicly traded in the São Paulo Stock Exchanges between the third quarter of 2018 (3Q2018) and the third quarter of 2020 (3Q2020). This period encompasses before and during the pandemic. All previous efforts (Fig. 1 ) worked with quarterly data. According to Kroes et al. [10] , when a study focuses on the firm performance implications of operational slacks, quarterly data is justified because it will be more accurate to reflect the reactions. The reported quarterly firm data is associated with the calendar date (the first quarter ends in March, the second in June, the third in September, and the fourth in December). Retail firms were identified as those classified as "Commerce", "Commerce & Distribution", and "Fabric, Clothing, and Footwear".
Initially, 31 companies were identified. Subsequently, their reference forms 1 were consulted, and those companies that were not within scope were excluded. Only companies, which incorporated the direct management of e-commerce before the pandemic, were considered. This resulted in a sample of 21 companies and 189 financial observations of each of the six model's measures -initial inventory cost (IIC), final inventory cost (FIC), net operating income (NOI), cost of goods sold (COGS), cost of the purchased product (CPP) and plant, property, and equipment (PPE). The data is in the local currency, the Brazilian Real (BRL). The adopted inflation rate was the IPCA (Broad Nacional Consumer Price Index), and the historical series were retrieved from official sources  [11, 27, 57]. As per previous authors  [10] , the current dataset presents a subtle difference from prior ones, which is the examination of firms at the quarterly versus annual levels.
In this way, it is believed that the proposed approach can also contribute to an index that better reflects the reactions of an efficient market and can provide an adequate tool for future investigations regarding operational efficiency and market response. A summary description of the analyzed companies is presented in Table  1. Once a novel model based on linear programming is proposed and the data is collected, we develop and run a new algorithm in Matlab software to solve it using a linear programming solver because it is one of the most common solving software for linear programming and operations research  [67]. Readers who are interested in using already established models are recommended to the interactive online app deaR-Shiny, based on the R software  [68].
Homogeneity and online retailing Here, the DMUs are publicly traded retailing companies that sell through physical stores as well as virtual channels. In this way, they are assumed to be homogeneous. Data from six measures (of the 21 DMUs) were used, respecting the recommended practice of keeping the number of measures smaller than three times the number of DMUs  [69]. For identifying the strategies of marketplaces, the reference forms were consulted. And it was checked through internet research engines whether it was possible to buy products online as described in the companies' official reports. The name of the companies and the name of their brands were searched on Scopus. In this way, it was analyzed the existence of applied research.
Acknowledgement The author  Dr Paulo Nocera Alves Junior  is supported by  Agencia Nacional de Investigación y Desarrollo (ANID) ,  Fondo Nacional de Desarrollo Cient. ífico y Tecnológico (FONDECYT) Iniciación en Investigación 2023 , Project  11230508. The author  Dr Marcelo Seido Nagano  is supported by grant number  312585/2021-7  from the  Conselho Nacional de Desenvolvimento Cient. ífico e Tecnológico (CNPq) -Brazil e Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) -Brazil.
III. SIMULATION p → νK + p → νK + p → νK + p → νK + p → νK + To understand the behavior of and discriminate it from the backgrounds in the JUNO detector, an MC simulation is performed in two steps: generator production and detector simulation. The generator of and its backgrounds is produced with GENIE (version 3. 0. 2)  [18] , in which the primary processes of and the atmospheric ν interactions in the LS are simulated. The detector simulation, which is a simulation of the final states of and the atmospheric ν interaction in the JUNO detector, is processed in SNiPER  [19] , which is a Geant4  [20]  based simulation software developed by the JUNO collaboration. All related optical processes, including the quenching effect, are considered. The profiles of the LS, including the fluorescence times can be found in Ref.
5 years of JUNO data collection or 890 kton-years exposure mass. Each atmospheric ν event has a weight value, which indicates the possibility of this event occurring for JUNO's 200 kton-years exposure considering neutrino oscillation. Then, these atmospheric ν events are simulated in SNiPER as our sample database. p → νK + Atmospheric ν events can be classified into the following four categories  [28] : charged current quasi-elastic scattering (CCQE), neutral current elastic scattering (NCES), pion production, and kaon production. The categories and their ratios are shown in Table  1. The most dominant backgrounds in the energy range of (sub-GeV) are formed by elastic scattering, including CCQE and NCES events. The final states of the elastic scattering events usually deposit all of their energy immediately, eventually followed by a delayed signal.
IV. ANALYSIS p → νK + To quantify the performance of background discrimination, we design a series of selection criteria to evaluate the detection efficiency of and the corresponding background rate based on the simulation data sample. According to the physics mechanisms introduced in the previous section, the key part of the selections is based on the triple coincidence signature in the hit time spectrum. Many beneficial studies searching for pro-p → νK + ton decay with an LS detector have been discussed by the LENA group and performed by the KamLAND collaboration  [13, 14]. However, the situation in JUNO is more challenging because of the considerably larger detector mass compared to KamLAND. Owing to the relative masses, in ten years, the detected number of atmospheric ν would be approximately 20 times that of the Kam-LAND experiment.
After applying these criteria, the total efficiency for is estimated to be 36. 9%, and only one event in Sample 1 remains from the simulated 160 k atmospheric ν events (corresponding to an exposure of 890 kton-years or an exposure time of 44. 5 years at the JUNO site). Because the volume cut in the basic selections provides a selection efficiency of 96. 6% to the total efficiency, it will not be counted in the exposure mass calculation. The three samples contribute 27. 4%, 7. 3%, and 2. 2% of the detection efficiencies, respectively. Considering the statistical error and weighting value, which accounts for the oscillation probability, the background level corresponds to 0. 2 events, which is scaled to 10 years of data collection by JUNO. χ 2 R χ ≡ χ 2 S /χ 2 D p → νK +.
Another uncertainty originates from the number of neutron capture, which can be affected by the secondary interactions of the hadronic daughter particles of atmospheric neutrino events in the LS. This is estimated as 10% assuming the same uncertainty as Super-K  [30]. The statistic uncertainty is estimated following the rule. Considering that only one event survives in the selection, it is calculated as in ten years. With 160 k events in the current MC simulation, this is difficult to improve because it will consume vast computing resources. We hope to update this value with a larger MC simulation data volume when it permits. Consequently, the background is estimated as. p → νK + The sensitivity for is expressed as is the total number of protons (including free protons and bound protons) in the JUNO CD, T is the running time, which is assumed to be 10 years to achieve an exposure mass of 200 kton-years, is the total signal efficiency, and is the upper limit of the 90% confidence level of the detected signals.
This depends on the number of observed events and the background level. According to the  τ/B(p → νK + ) = N p T ϵ n 90 , (7) p → νK + Compared to the representative LS detector, the detection efficiency on of JUNO is relatively lower than that of LENA  [13]. This is reasonable considering that the study is based on an overall detector simulation of JUNO. Based on the background level of 0. 02 events per year, we plot the JUNO sensitivity as a function of running time, as shown in Fig. 12. After six years of running (120 kton-years), JUNO will overtake the current best limit of the Super-K experiment. Moreover, the proton lifetime measured by JUNO will reach years for the first time after 10. 5 years of data collection. In the case of no event observation after ten years, the 90% C. L. limit on the proton lifetime would reach years.
Recently tree fruits crop type mapping was conducted in Egypt by examing various temporal windows, spectral approaches, and several combination methods between S1 (Sentinel-1) and S2 (Sentinel-2) data inserted into RF  [21]. Good accuracy was found with S2 alone while improvement was found by combining the textural S1 information with the spectral S2 observations, which led to an OA of 96%. In  [22]  a classification was done to delineate apple orchards, vineyards, and annual crops in Iran. Phenology was used to select the optimal dates. By combining S1, Landsat 8 images, and digital elevation model, an OA of 89% was obtained. Another recent study was conducted in Juybar, Iran where an automatic approach to map citrus orchards was implemented using S1 and S2 and the ALOS digital surface model (DSM)  [23].
Satellite Data In our study, time-series of Sentinel-2 (S2) optical images were utilized and were obtained from both Sentinel-2A and Sentinel-2B of all dates in a given year within the 2016-2021 period considering the visible (B2, B3, and B4), the near-infrared (B8 and mid-infrared (B11 our B12) bands. We utilized the open-source service center to obtain images (https://www. theia-land. fr/, accessed on 17 May 2022), it offers cloud treatments (cloud mask) to eliminate pixels influenced by clouds (images with > 30% cloud cover), and for this obvious reason, the number of images utilized vary across study sites and years. Since Sentinel-2B satellites were functional in the time of 2017, lesser dates were obtained from the 2016-2017 year. The number of cloud-free images (with <30% cloud cover and subsequently masked) used for each year across the two study sites is reported in Table  2.
Finally, the model output is decided by the number of a majority of votes by the classifier ensembles. Regarding remote sensing and land surface phenology mapping, the RF classifier remains an effective approach  [41]  thus, for classification accuracy assessments ground truth information was equally split into two batches for each class namely calibration (50% proportional distribution from each class in the target population) and evaluation (50% from each crop class) datasets utilizing a spatial cross-validation method from the CAST package in R  [63]. The aforementioned spatial crossvalidation aid ensures the selected ground truth data of a similar field will be apportioned either in the calibration or evaluation dataset to keep away from over-fitting. Accuracy evaluations were done by the confusion matrix which gives the number of plots well classified on the diagonal and the number of erroneous detection between classes outside the diagonal with predicted class in column and actual class in line  [64].
 The THEIA land use map is yearly implemented across the entire French national territory (https://www. theia-land. fr/ceslist/ces-occupationdes-sols/, accessed on 17 May 2022). It uses RF-supervised classification on all S2 dates (VIS and NIR bands) with other supplementary data like urban maps, topography, Corine Land Cover map, and 'Registre Parcellaire Graphique ' (RPG) that collect yearly farmer's declarations on subsidy collection from the European Union (EU). The number of identified classes was seventeen of which OC (tagged as 221 and 14 in 2017 and 2018 respectively) and VY (tagged as 222 and 15 in 2017 and 2018 respectively) are inclusive and in our study, the detection of orchards and vineyards were assessed. The maps were prepared at 10 m pixels of S2 and aggregated at the field level to be comparable with the results of our study.
Former studies highlighted that large data samples bolster RF classification accuracy. while in some situations, larger data samples might lower the RF classification accuracy pending on the dataset quality. In our study, we obtained good accuracy using a small training data set of 117 (out of 1601 in Ouveze-Ventoux) and 122 (out of 16680 in Crau) This corroborates the conclusions of Nguyen et al. [33]  and Colditz  [67]  who found that datasets consisting of 0. 15% to 0. 35% of the study sites are sufficient to attain a precise land cover delineations. As a rule of thumb, studies related to land use/land cover should operate with restricted data because of the excessive price of organizing field data. Therefore, to reduce the ground truth collection burden, we can imagine applying the RF model determined for a given year to the other years.
Such drawbacks are minimized by considering only OC and VY plots with sufficient development, which led us to put thresholds in our reference dataset by assimilating several young OC and VY to the DC class. To obtain an optimal result this thresholding depends on the considered area, which is probably the reflection of different cultural practices for the management of the interrow. The determination of these thresholds is a real limitation of the method. There is therefore a strong stake to work on the separation of the contributions coming from the inter-row vegetation and the canopy of the stand of interest. A finer analysis of the spectral signature and/or a finer analysis of the temporal dynamics could contribute to a better separation of these two components and thus work on the specific signal of the crop of interest.
However, the methodology is applicable in both sites but with some adaptations to take into account the difference in phenology calendar and specific practices The obtained performances led to an overall accuracy ranging between 0. 89 and 0. 96 and a Kappa index ranging between 0. 87 and 0. 95. This is far better than the results we can obtain by applying the RF method on LAI time series (the same used to infer the phenology metrics) and significantly better than the THEIA classification which is an operational tool implemented over the France territory using multiple sources of ground information. The parsimony in ground truth is a strength of the approach which can be operated with a small database (less than 1% of the plots are required to inform classification). Moreover, as the method is independent of the satellite acquisition dates, we can apply a RF classification model obtained from one year to the other years while keeping a reasonable accuracy.
Data Availability Statement: Sentinel 2 data are available at the following hyperlink: https://www. theialand. fr/, accessed on 17 May 2022. THEIA classification data can be found at the following hyperlink: https://www. theia-land. fr/ceslist/ces-occupation-des-sols/, accessed on 17 May 2022. Inglada, Jordi, Vincent, Arthur, & Thierion, Vincent. (2018). Theia OSO Land Cover Map 2018 [Data set]. Zenodo. https://doi. org/10. 5281/zenodo. 3613415 (accessed on 17 May 2022). plot boundary map and evaluation data over the CRAU and OUVEZE-VENTOUX are available on request from the corresponding author.
As the vast majority of excavated archaeofaunal skeletal remains are fragmentary to the extent that they cannot be identi ed by morphological analysis alone, various molecular methods have been considered to retrieve information from an otherwise underutilised resource. The introduction of collagen ngerprinting, known as Zooarchaeology by Mass Spectrometry (ZooMS), has become one of the most popular approaches to improve taxonomic data yields from fragmentary bone. However, few studies have analysed large numbers of samples. Here we test the incorporation of liquid-handling robots to further develop ZooMS into a more automated technique using samples excavated from Grotte Mandrin, France. By increasing the faunal identi cations of the B2 layer (~42-44 Ka) at Grotte Mandrin from 55 to 1037, we identi ed a wider range of taxa, now including bear and mammoth, as well as further remains of hominin.
Research at Mandrin began in 1990 and the shelter has since undergone multiple excavations. At present ~ 70,000 archaeofaunal remains have been excavated, a large proportion of which are fragmentary and unable to be identi ed by morphological analysis (this proportion varies with taxonomic level and the type of site, but typically 70-90% of recovered archaeological remains are not identi ed beyond family level). The extensive archaeofaunal assemblage at Grotte Mandrin is a great exemplary candidate for the need of an automated ZooMS technique. A sizeable investment of time and funding has allowed for Grotte Mandrin to be excavated for the last three decades. In this study we describe an automated ZooMS (AutoZooMS) approach that we use to help ensure maximal taxonomic data yields can be secured from this investment.
The aims of this study were to explore the inclusion of liquid handling robots into the ZooMS protocol, both for improving accuracy, repeatability, and robustness (e. g. , to multichannel pipette clogging error), hereafter referred to as 'AutoZooMS', which is the HT method adapted for automation. We hope that the fast-processing power of this technique will promote increased utilisation of excavated archaeofaunal material to increase taxonomic data yields, reduce archaeofaunal waste and encourage larger sample sizes in future zooarchaeological research. AutoZooMS has been developed using the archaeofaunal assemblage at layer B2, Grotte Mandrin, thus the secondary aim of this study has been to conduct a zooarchaeological analysis of this site.