dataesr/scientific-paragraphs-categorization huggingface dataset:
Labelling Issue As noted above, the datasets for the actual houses are labeled by the residents themselves, via a logbook or graphical user interface. They are then post-processed by the responsible researchers. However, it is not impossible that some labels may be missing as in the CASAS Milan dataset  [75]. Table  4  presents an extract from the Milan dataset where labels are missing. However, events or days are duplicated, i. e. same timestamp, same sensor, same value, same activity label. A cleaning of the dataset must be considered before the algorithms are formed. Obviously, depending on the quality of the labels and data, the results will be different. Indeed some occurrence of classes could be artificially increased or decreased. Some events could be labeled "Other" even though they actually belong to a defined activity.
In this case the recognition algorithm could label this event correctly but it would appear to be confused with another class in the confusion matrix. Since HAR is a multiclass classification problem, researchers use metrics  [109]  such as Accuracy, Precision, Recall, and F-Score to evaluate their algorithms  [41, 49, 61]. These metrics are defined by means of four features such as true Positive, true Negative , false Positive, and false Negative of class C i. The F-score, also called the F1-score, is a measure of a model's accuracy on a dataset. The F-score is a way of combining the Precision and Recall of the model, and it is defined as the harmonic mean of the model's Precision and Recall. It should not be forgotten that real house datasets are mostly imbalanced in terms of class. In other words, some activities have more examples than others and are in minority.
Evaluation Process 7. 2. 1. Train / Test A first way to evaluate the algorithms is to divide the datasets into two distinct parts. One for training and the other for testing. It is generally chosen to use 70% for training and 30% for testing. Several researchers have chosen to adopt this method. Surong et al. [16]  have adopted this evaluation method in the application of real time activation recognition. In order to show the generalization of their approach, they chose to divide the datasets temporally into two equal parts. Then to re-divide each of these parts temporally into training and test datasets. They thus propose two sub-set of training and test. The advantage of this method is that it is usually preferable to the residual method and takes no longer to compute. Moreover, it does not allow to take into account the drift  [34]  of the activities.
K-fold Cross Validation This is a wide approach used for model evaluation. It consists of dividing the dataset into K sub dataset, the value of K is often between 3 and 10. K-1 dataset are selected for training and the remaining dataset for testing. The algorithm iterates until all the sub dataset is used for testing. The average of the training K scores is used to evaluate the generalization of the algorithm. It is usually customary that the data is mixed before being divided into K sub datasets in order to increase the generalization capability of the algorithms. However it is possible that some classes are not represented in the training or test sets. That's why some implementations propose that all classes are represented in tests as not training. In the context of HAR in smart homes, this method is a good approach for classification of EW  [14, 61].
Leave-One-Out Cross-Validation This is a special case of cross-validation where the number of folds equals the number of instances in the data set. Thus, the learning algorithm is applied once for each instance, using all other instances as a training set and using the selected instance as a single-item test set. Singh et al. [60]  and Medina-Quero et al. [49]  used this validation method in a context of real-time HAR. In their experiments the dataset is divided into days. One day is used for testing while the other days are used for training. Each day becomes a test day in turn. This approach allows a large part of the dataset to be used for training. Allowing the algorithms to train on a wide variety of data. However the size of the test is not very significant and does not allow to demonstrate the generalization of the algorithm in the case of HAR in smart homes.
Multi-Day Segment Aminikhanghahi et al. [34]  propose a validation method called Multi-Day Segment. This approach proposes to take into account the sequential nature of segmentation in a context of real-time HAR. Indeed, in this real-time context, each segment or window is temporally correlated. According to Aminikhanghahi et al, and as expressed above, cross validation would bias the results in this context. A possible solution would be to use the 2/3 training and 1/3 test partitioning as described above. However, this introduces the concept of drift into the data. Drift in terms of change in resident behavior would induce a big difference between the training and test set. To overcome these problems, the proposed method consists of dividing the dataset into 6 consecutive days. The first 4 days are used for training and the last 2 days are used for testing.
Outlines Different validation methods for HAR in smart homes was reviewed in this section, Table  5. Depending on the problem being addressed, not all methods can be used to evaluate an algorithm. In the case of offline HAR i. e. with EW or pre-segmented activity sequences, the K-fold cross-validation seems to be the most suitable. Provided that the time dependency between segments is not taken into account. Otherwise, it is preferable to use another method. The Leave-One-Out Cross-Validation approach is an alternative. It allows to process datasets containing few data. But the days are considered as independent. It is not possible to make a link between two different days e. g. a weekday or a weekend day. Aminikhanghahi et al. [34]  have proposed a method to preserve the temporal dependence of the segments and avoid the problem of data drift induced by changes in the habits of the resident(s) over time.
Ref In addition, the preprocessing of dataset data, the rebalancing, the removal of the "Other" class and the annotation of events affect the algorithms' performance. It is therefore important to take into account the evaluation method and the preprocessing performed, in order to judge the performance of the algorithm. Moreover, classic metrics such as accuracy or F score may not be sufficient. It may be more judicious to use, metrics weighted by the number of representations of dataset classes such as dataset are unbalanced. Balanced accuracy, or F1 weighted score should be a better metric in this case  [110, 111]. A major problem in the area of HAR in smart homes is the lack of evaluation protocols. Establishing a uniform protocol according to the type of problem to be solved (real-time, offline) would speed up research in this field and allow a fairer comparison between the proposed algorithms and approaches.
Comparison with Other HAR While human activity recognition algorithms have seen tremendous improvements for vision-based data owing to the rapid development of deep learning for image processing, human activity recognition using wearables and sensors on objects are also seeing significant improvements. However, vision-based systems are seen by users as too intrusive as these systems could unveil too much private information, whereas wearables and sensors on objects require the daily instrumentation of the sensors on the body of subjects or their personal objects, ambient sensors could provide a solution to tackle this issue. HAR in smart homes have seen recent advances owing to the development of recent deep learning algorithms for end-to-end classification such as convolutional neural networks.
However the sequence analysis challenges still remain largely unsolved because of the impact of the sparsity and irregularity of the data on context understanding and long-term reasoning. In particular, it makes the challenges of composite activities (sequences of activities), concurrent activities, multi-user activities recognition and data drift more difficult. The sparsity of the data also makes it more difficult to cope with the variability of the smart home data in its various settings. According to our analysis, the state of the art in HAR for ambient sensors are still far from ready to be deployed in real-use cases. To achieve this, the field must address the shortcomings of datasets, but needs also to standardise the evaluation metrics so as to reflect the requirements for a real-use deployment and to enable fair comparison between algorithms.
Opportunities Moreover, we believe that recent advances in machine learning from other fields also offer opportunities for significant advances in HAR in smart homes. We advocate that the application of recent NLP techniques can bring advances in solving some of these challenges. Indeed, NLP also deploys methods of sequence analysis, and has also seen tremendous advances in the recent years. For instance, sparsity of the data can be alleviated by a better domain knowledge in the form of an emerging semantic. Thus taking inspiration from word encoding and language models, we can automatically introduce semantic knowledge between activities, as shown in the preliminary study  [46]. Furthermore, a semantic encoding of the data will also help the system be more robust to unknown data as in the challenges of data drift or adaptation to changes, as it could be able to relate new data semantically to known data.
Discussion In this review, we have pointed out the key elements for an efficient algorithm of human activity recognition in smart homes. We have also pointed out the most efficient methods, but also the remaining challenges and present opportunities. However the full deployment of smart home services, beyond the HAR algorithms, depend also on the development of the hardware systems and the acceptability and usability of these systems by final users. For the hardware systems, the development of IoT devices with the improvement in the accuracy and autonomy along with the decrease in their cost will make them accessible to normal households. Despite cheaper sensors and actuators, it will not be realistic to provide all homes with a large set of sensors as in the current datasets, but real homes are not equipped as lavishly.
Based on Galileo metadata published by the European GNSS Agency, we developed the Galileo a priori macromodel that considers the detailed construction and surface properties of satellite buses and solar panels to account for the direct SRP, earth's albedo, and earth's infrared radiation. Similar satellite models based on empirically adjusted   (Lyard et al. 2006)  surface parameters provided by IGS are employed for GPS and GLONASS satellites due to the lack of official detailed information about the satellite construction for all military spacecraft. The uncertainty of the box-wing model for Galileo is about 3%  (Bury et al. 2020) , whereas the remaining fluctuations of SRP, as well as small accelerations caused by and the misalignment of the solar panels with respect to the direction of the sun when assuming the yaw-steering mode are accounted for by estimating empirical orbit parameters using the 5-parameter ECOM model.
Odds and risk ratios associated with C. perfringens and C. colinum presence in healthy birds After identifying clostridial enteritis in two separate lorikeet flocks, we then used the CZA lorikeet data to explore factors that could be linked to susceptibility. First, we examined 16S rRNA reads for the presence of C. perfringens and C. colinum in healthy birds to determine whether they were predictors for developing future enteritis. Healthy CZA birds that had C. perfringens (n = 24, 36%) or C. colinum (n = 7, 11%) present in their gut were not at increased odds or risk of developing future enteritis (C. perfringens: OR = 0.
Susceptibility to enteritis Besides age, seasonality has also been linked to necrotic enteritis in birds. In poultry, enteritis cases occur more frequently in late winter and early spring  [83] [84] [85] , which is what we observed in the CZA lorikeets; although historically, CZA reported enteritis cases across all seasons. DZ cases were also clustered during winter months, however this was the only occurrence of an enteritis outbreak DZ had experienced. Additionally, birds that had a history of enteritis were at 2. 2 times increased risk of developing future enteritis. Gross pathology and histopathology data further linked signs of chronic lesions (fibrosis, mucosal atrophy, villus blunting/fusion, wasting) to birds with repeated cases of enteritis. Repeated bouts of enteritis in some CZA lorikeets likely resulted in permanent intestinal damage that hindered absorption and peristalsis, leading to malnutrition and intestinal stasis, which are known risk factors for C.
16S rRNA sequence processing The 16S rRNA sequences were processed, filtered, and analyzed using QIIME 2 version 2020. 11 [101]  and DADA2  [102]. Taxonomic assignment of amplicon sequence variants (ASVs) was performed using the Greenegenes 13_8 database with 99% sequence identity cutoff. (Note: We also performed taxonomic assignment with SILVA 132 and found that, in this case, Greengenes provided more specific taxonomic assignments, particularly in the Clostridia taxa. ) A total of 246 samples from CZA and 30 samples from DZ were submitted for sequencing. Samples with fewer than 1000 reads were removed from analyses including 23 CZA samples and 6 DZ samples. This resulted in a total of 223 CZA samples and 24 DZ samples that were used in our analyses. After filtering, we obtained a total of 3,236,674 reads from the CZA samples (average: 13,911 reads per sample; range: 1003 to 68,537 reads) and 264,769 reads from the DZ samples (average: 9,026 reads per sample; range: 1922 to 26,766 reads).
Consent for publication LG and SP were involved in Random Forests and LASSO analysis and interpretation, manuscript writing, and figure preparation. KF-K, MM, KLP, GB, and FU were involved in histopathology analysis, staining, and interpretation. MM and FU were also involved in manuscript writing and figure preparation. KF-K and MM were involved in gross pathology interpretation and figure preparation. FU performed histopathology, IHC analysis, interpretation, and figure preparation. MN performed toxinotyping on FFPE blocks, C. colinum PCR, and was involved in manuscript writing and table preparation. WC, KS, and YV were involved in trypsin inhibitor testing and data interpretation. WC and YV were also involved manuscript writing and figure preparation. We would like to acknowledge all of the keepers and veterinary staff at the Columbus Zoo and Aquarium and the Denver Zoo for facilitating sample collection and providing detailed health, medication, and dietary record.
Acknowledgements We thank members of the  Massachusetts Host-Microbiome Center , especially  Vladimir Yelisheyev  and  Rosa Perez Gonzalez , for assistance with GF mouse procurement and husbandry. We also thank the members of the  Waldor lab  for helpful discussions during this study and comments on the manuscript. Research in the Waldor Lab is funded by the  National Institutes of Health  ( R01-AI-042347 ), the  Wellcome Trust  ( 218443/Z/19/Z ) and the  Howard Hughes Medical.
Dating recent sediment archives (< 150 years) constitutes a prerequisite for environmental and climatic reconstructions. Radiocaesium ( 137 Cs) emitted during thermonuclear bomb testing (∼ 1950-1980)  and nuclear accidents and the decrease in excess lead-210 ( 210 Pb xs ) with depth are often combined to establish sediment core chronology. Although these methods have been widely used during the last several decades, there is a lack of structured and comprehensive worldwide synthesis of fallout radionuclide analyses used for dating sediment cores in environmental and Earth sciences. The current literature overview was based on the compilation of 573 articles published between 1977 and 2020, reporting the collection of 1351 individual dating sediment cores (the dataset can be accessed at https://doi.
org/10. 1594/PANGAEA. 931493; Foucher et al. , 2021). This review was conducted in order to map the locations where 137 Cs fallout events were detected. These included the thermonuclear bomb testing peak in 1963, the Chernobyl accident in 1986, the Fukushima accident in 2011, and 24 additional events identified at 112 sites that led to local or regional radioactive releases (e. g. , Sellafield accidents, Chinese nuclear tests). When 210 Pb xs records were used along with 137 Cs data, detailed information on the 210 Pb xs age-depth models were also synthesized. With the current growing number of studies analyzing sediment cores and the increasing interest in the deployment of sediment fingerprinting techniques including radionuclides as potential discriminant properties, this spatialized synthesis provides a unique worldwide compilation for characterizing fallout radionuclide sources and levels at the global scale.
Literature overview Journal articles using 137 Cs for dating sediment cores published in English were extracted from the Thomson Reuters Web of Science database until 29 February 2020. The search words " 137 Cs" and "sediment core" were used in combination. A total of 910 publications were found by the automatic literature searching tool starting from 1977. Soil science studies focusing on the depth distribution of 137 Cs in soils  (Jagercikova et al. , 2014)  and publications reporting 137 Cs measurements conducted on surface soil layers or deploying sediment fingerprinting approaches using 137 Cs as a diagnostic property  (Evrard et al. , 2020)  were excluded from further analysis and removed from the database. Among the remaining references, 573 publications were selected to feed a database including a total of 1351 individual dated sediment cores (Fig.
1 ) -(the database is freely downloadable from https://doi. org/10. 1594/PANGAEA. 931493). The database includes the following categories of information: publication metadata (site name, author information, DOI or permanent link, journal title, publication year, main processes studied, GPS coordinates, country) sampling information (sampling year, core length, coring environment -e. g. , lacustrine, coastal, floodplain, corer type, corer diameter, subsampling increment, sediment compaction measured during the core collection) In this meta-analysis, both 137 Cs and 210 Pb xs activities and inventories were systematically decay-corrected from the sampling date to 1 January 2020 to allow comparisons between cores.
Publication characteristics Studies included in the database mainly provided dating of lacustrine sediment (47 %) and coastal-bay-estuarine sequences (29 %) and to a lesser extent, marine sediment (9 %), floodplain-riverine sediment (8 %), marsh-wetlandpeatland sediment (4. 5 %) and lagoon sediment (2. 5 %). These studies were published in a large variety of journals (n = 153) dealing mainly with geosciences, chemistry and environmental radioactivity. The most frequently encountered journals in the database are the Journal of Environmental Radioactivity (n = 49), Science of the Total Environment Studies compiled in the database mainly focus on contaminant-pollution-related topics (in 31 % of the publications) followed by investigations of sedimentation rates (26 %), monitoring of radionuclide concentrations in the https://doi.
Core subsampling and data availability Information about the total core length was present in 68 % of the publications. This information was given in figures, tables or directly in the text. On average, the core length reached 0. 85 m (SD: 1. 8 m). The subsampling mode was detailed in 72 % of the publications, with an average increment thickness of 2. 1 cm (SD: 1. 5 cm). In 73 % of the publications, the analytical method used to analyze 210 Pb xs was provided. Alpha spectrometry was used in 33 % of the studies, whereas gamma spectrometry was used in the rest of the publications. As shown in Fig. 3 , a shift between both analytical techniques was observed around 2005, due to the analytical developments made during this period and the improvement of gamma spectrometry potential at low energies, including at 46 keV, i.
The lowest activities attributed to this accident were mostly located in western Europe (e. g. , United Kingdom) and in southeastern Europe (Greece, Turkey). On average, this event was detected at a 30 cm depth (SD: 59 cm, range: 1 to 420 cm) in the sediment cores. The discrimination between fallout that occurred in 1963 and after Chernobyl can be achieved through the analysis of 239+240 Pu and 241 Am. Both radionuclides were respectively searched for in 12. 7 % and 11 % of the sedimentary sequences compiled in this database. Accordingly, 241 Am was found in 30 % of studies where the 1963 fallout alone was identified. Finally, fallout associated with the Fukushima accident was identified in 1. 9 % of the sediment cores (n = 26). This level was mainly detected in mainland Japan or along the Japanese eastern coast (Fig.
4 ), although it was also surprisingly identified in Mexican and Ghanaian sediment sequences. The average activity associated with this fallout was 1920 Bq kg -1 (SD: 7780 Bq kg -1 , range: 6 to 35 700 Bq kg -1 ). This level was identified at an average depth of 1. 8 cm (SD: 3 cm, range: 0. 25 and 11. 5 cm) in the sediment sequences. In addition to fallout attributed to the Fukushima and Chernobyl accidents and the atmospheric nuclear tests, 24 other local or regional events that have released radionuclides into the environment were identified by the authors and listed in the current database (Table  1 ). Among these events, emissions following the Sellafield nuclear plan accidents were observed in 40 sequences (3 % of the sediment cores covered in the database), the fallout associated with the Mayak Production Association (Russia) in 12 sediment cores (0.
Latitudinal distribution of 137 Cs in sediment cores The maximal activities associated with the atmospheric nuclear tests (activities generally attributed to 1963) were recorded between the latitudinal bands comprised between 40 and 60 • N, with an average activity of 90 and 117 Bq kg -1 , respectively. Values were not available for the bands located between 0-10 • , 50-60 • and 80-90 • S. This is consistent with the United Nations Scientific Committee of the Effects of Atomic Radiation (UNSCEAR, 2000) compilations recording the highest inventories between 40 and 60 • N for this event (inventories reached respectively 1186 and 1383 Bq m 2 ). Distribution of both databases were similar, with a strong correlation coefficient between inventories and activities (r 2 = 0. 87, p value < 0. 05) -(Fig.
210 Pb xs detection and use This natural radionuclide was often used in combination with 137 Cs in this database (in 64 % of the studies). In these publi-cations, the core chronology based on 210 Pb xs was estimated with seven different models: constant rate of supply (CRS)  (Appleby and Oldfield, 1978) , constant initial concentration (CIC)  (Pennington et al. , 1976) , constant flux constant sedimentation (CFCS)  (Krishnaswamy et al. , 1971) , periodic flux model (PF)  (Sanchez-Cabeza et al. , 2000) , constant initial concentration and constant sedimentation rate (CICCS)  (He and Walling, 1996) , constant rate of accumulation (CRA) and constant initial activity (CIA)  (Robbins and Herche, 1993). Among the most employed tools, the CRS model (also referred to as the CRSMV model in some publications) was applied in 54 % of the studies, the CIC model (also designated as CA model) in 23 % of the articles and the CFCS model (also called the CF, CSM, CFS or RSSM model with some of them including limited adaptations) in 20 % of the publications.