dataesr/scientific-paragraphs-categorization huggingface dataset:
The scores where obtained with the Linear S→V model due to its good results, robustness and simplicity. To account for the noise caused by to the randomness of the removed attributes, each reported score is the average of 10 measurements, each with different random attributes removed. The remaining attributes are 2-normalized, and the hyper-parameter is re-selected by cross-validation for each run. Fig. 1  provides a visualization of the result; a table with the exact scores is available in the supplementary materials. On CUB, there is still a substantial margin for improvement; even though our method enables a significant increase over other methods, it is still barely above results achievable by selecting only 20 attributes among the 312 initial attributes. Interestingly, the difference between webly supervised and hand-crafted prototypes is not so pronounced on the AwA2 dataset; the ZSL accuracy between the two settings is even surprisingly close.
Conclusion We proposed a new method to build semantic class prototypes automatically, thus enabling to better address large scale ZSL. Our results indicate that appropriately learning embeddings on specialized collections made of photo metadata is better than exploiting generic embeddings as it was done previously in ZSL. This still stands when generic embeddings are learned with collections which are two to three orders of magnitude larger than specialized collections. Among photo metadata based collection, the use of Flickr seems preferable to that of metadata associated to photos from Web pages. This is notably an effect of a better semantic coverage of classes in Flickr compared to ClueWeb12. We will release the specialized embeddings created here, as well as the visual features and the code necessary to produce the reported results in order to facilitate reproducibility.
We also use a pre-trained ResNet to extract visual features for the CUB and AwA2 datasets, and we further apply 10-crop to the images. On ImageNet and CUB, hyper-parameters of ZSL methods are selected using respectively 200 and 50 random classes for validation. For AwA2, we use the 8 classes which are not in the ILSVRC out of the 40 training classes.
D Effect of combining f l cust and f l wiki In Subsection 4. 2 of the main paper, we noted that f l cust , the Flickr collection which includes metadata from the three test datasets, gave the best results  among the text collections tested. Since f l wiki is collected from the same source but with a different set of concepts, we merged the two collections to observe the effect of results. The results are reported in Table  8  and they confirm that most of the performance gain is due to the use of f l cust.
F Performance gain of f l cust over wiki We present a comparison of FastText accuracy obtained for wiki and f l cust for the ImageNet dataset with different models. Figure  1  provides a view of accuracy   differences between f l cust and wiki for ImageNet test classes. These differences are plotted in decreasing order from left to right. For the Linear S→V model, f l cust is better for 265 of ImageNet test classes, no change is observed for another 99 classes and wiki provides better results for the remaining 136 classes. For classes that perform better with f l cust , the average gain is 0. 13 and the maximal gain is 0. 88. For those performing worse, the average loss is -0. 08 and the maximal loss is -0. 4. Trends are similar for other methods, indicating that performance gains are robust with respect to the ZSL methods used.
). Examples of classes at both ends of the spectrum are visible in Figure 3: unseen class morel (on the left) is close to seen class agaric and has no unseen siblings; its class accuracy is 0. 63. On the other hand, classes holly, teak and grevillea (on the right) have many unseen siblings and are far from any seen class; their respective accuracy are 0. 01, 0. 00 and 0. 03. More generally, classes which are descendant of the intermediate node woody plant have an average accuracy of 0. 053. The full graph visualization of the 1000 training classes, 500 testing classes and intermediate nodes of the ImageNet ZSL dataset is provided in the supplementary materials.
Results with and without 2 normalization of attributes on the ImageNet dataset; this table is similar to Table 1 of the main paper. Normalized attributes are indicated with the norm exponent; results without the exponent correspond to unnormalized attributes. flwiki flcust pt wiki clue flwiki flcust pt wiki clue flwiki flcust LinearV →S 2. 0 4. 3 4. 1 3. 7 4. 6 5. 4 3. 3 1. 3 4. 2 5. 3 1. 8 4. 6 1. 1 4. 0 4. 9 LinearS→V 10. 7 12. 1 12. 5 12. 4 17. 0 14. 3 7. 7 8. 7 8. 2 10. 6 14. 6 12. 5 2. 5 12. 8 17. 3 ESZSL norm 13. 4 12. 8 13. 6 13. 8 18. 0 16. 1 10. 7 11. 9 13. 7 14. 4 16. 0 13. 0 8. 6 14. 7 17. 7.
Results with and without 2 normalization of attributes on the CUB and AwA2 datasets; this table is similar to Table 2 of the main paper. Normalized attributes are indicated with the norm exponent; results without the exponent correspond to unnormalized attributes. flwiki flcust pt wiki clue flwiki flcust pt wiki clue flwiki flcust CUB dataset LinearV →S 5. 6 12. 1 10. 7 11. 7 15. 7 3. 9 13. 4 5. 5 11. 5 12. 1 3. 2 12. 0 7. 9 11. 7 15. 2 LinearS→V 14. 3 19. 0 17. 7 20. 1 21. 3 20. 6 14. 3 12. 9 14. 9 17. 3 18. 0 17. 4 2. 0 19. 2 22. 4 ESZSL norm 16. 9 20. 6 16. 7 20. 9 23. 6 19. 1 18. 3 18. 8 21. 2 22. 0 20. 7 17. 4 19. 9 21. 5 24. 0 Awa2 dataset LinearV →S 27. 3 15. 6 33. 6 15. 5 25. 9 30. 6 17. 2 34. 9 26. 3 42. 3 7. 8 11. 8 9. 7 3. 8 15. 2 LinearS→V 24. 8 45. 0 53. 2 56. 1 56. 6 55. 7 48.
4 26. 9 34. 6 40. 5 43. 3 42. 1 39. 9 28. 1 38. 5 41. 6 Linear S→V 38. 1 44. 1 49. 7 53. 9 55. 0 56. 6 42. 4 48. 1 41. 2 57. 7 54. 7 49. 3 14. 4 50. 4 46. 5 ESZSL 40. 9 42. 2 55. 8 53. 1 57. 1 61. 4 37. 7 49. 0 48. 2 44. 3 48. 2 37. 6 7. 9 49. 7 54. 6 ConSE 27. 4 31. 3 34. 3 43. 3 39. 2 31. 3 27. 4 29. 8 38. 4 41. 4 34. 7 31. 3 16. 7 42. 3 42. 1 Devise 37. 2 34. 1 46. 6 33. 7 43. 4 43. 2 42. 6 44. 9 30. 6 36. 4 52. 0 40. 7 13. 5 32. 7 37. 6 SynC o-vs-o 43. 9 41. 1 45. 8 47. 1 47. 5 46. 9 46. 6 47. 4 50. 0 52. 1 53. 3 40. 0 15. 2 45. 5 48. 1 Model word2vec GloVe FastText Source pt wiki clue flwiki flcust pt wiki clue flwiki flcust pt wiki clue flwiki flcust CUB dataset Linear V.
Since the end of the nineties, Dutch census publications have been digitized and made available for digital processing. New analyses of the data were presented in some fruitful conferences in the first decade of this century. In addition to the census publications, a mass of detailed census data was found in dossiers and so-called "transparencies" in the archive of Statistics Netherlands. Most of that material was scanned into digital images, awaiting further content conversion into numeric data. In the present article, the authors describe the process of digitizing the detailed tables of the Dutch Population and Occupational Censuses held in 1947, which is the first set of detailed census data that is made available in a digitally processible form. They give an example of historical analyses made possible by this dataset.
(2007). The first article in that monograph gives an overview of the preceding decade of digitizing the Dutch census publications  (Doorn & van Maarseveen, 2007). In 2004 the digitized data were made accessible for a worldwide audience via the website www. volkstellingen. nl and in the dans data archive. The  10. 1163/24523666-bja10018 | jonker et al. documentation of the website data is available as online dans Data  Guide 16 -Volkstellingen 1795 -2001 (2019) , with references to all data in the dans data archive (www. doi. org/10. 17026/dans-xhh-p9qy). In the present article, we describe the process of digitizing the detailed tables of the Dutch Population and Occupational Censuses held in 1947, the first census held after World War ii. The publications of this census have been digitized in the above-mentioned projects.
In addition to the publications, the cbs administers detailed data for 1947 in hand-written tables on so-called "transparencies", about 30,000 sheets in A4 format. For later censuses, comparable detailed data already existed in digital form. We take the 1947 census as an example of preparing and publishing a large dataset. Our focus is on the challenges in digitizing such a volume of census data, in particular the processing of a large number of files. Digitizing costs are one element to be reckoned with: costs for scanning and the high costs for converting the scanned images into electronically processable data. Transportation is another aspect. The transportation of tens of thousands of vulnerable originals, the transparencies, from the depot to the scanning location and back is a logistic operation, involving an accurate organization and registration of the originals as well as of the scanned images.
Data -Thematic collection: 12th population census, 31 may 1947, deposited at dans -DOI:www. doi. org/10. 17026/dans-zs3-cf4m -Temporal coverage: 1947 The detailed tables of the Population Census 1947 are now available via the online archiving system of dans. The data are presented in a collection of 12 datasets, one per province plus a dataset for the Netherlands as a whole. Each dataset contains data and documentation, divided into four folders: three containing the data and one containing the documentation. Figure  1  shows the structure of the dataset for the province of Drenthe. The data consists of scans of the detailed tables in jpg format, xls sheets containing the data of the detailed tables created by data entry, and csv files, converted from the xls sheets. The scans and the output files in xls format are included as provenance data and to enable reproducibility.
The output files in csv-text format are best used for further analysis. detailed tables from the dutch census  | 10. 1163/24523666-bja10018  Within each dataset, the three data types have a separate folder. In this folder, the data is grouped by municipalities. Each dataset also contains documentation regarding the data: a description for the given dataset, a description of each detailed table, and the templates for the data entry. To give an example, Figure  2  is the scan of a transparency for Table  4  (and Table  5 ) of the city Alkmaar in the province of North Holland. Figure  3  shows the Excel sheet resulting from the data entry of Table  4. The list of tables of the 1947 census is, with minor variations, the same for each province. However, the degree of detail does vary within the provinces, where the larger cities are treated in more detail than the smaller villages.
A list of tables provides the best overview of the richness of the source and the subjects treated (see Appendix). The list makes clear that a great variety of detailed analyses of the Dutch population briefly after World War ii is possible with the digitized data. Household composition, age structure, religious denomination, marital status, housing conditions, employment and work situation, nationalities, commuting, and female participation in the labor force are just a selection of the topics that can be studied. Most of the information is available to a great geographic detail: not only for every one of the 1016 municipalities in 1947 but often for subdivisions of the municipality (neighborhoods, hamlets, etc. ). and Social Sciences (2021) 1-16  In section 4 of this article, we present one analysis of the dataset by way of example.
2. 1 Special Feature of the Dataset A special feature of this dataset is the presence of control counts in the output files. For any table that shows (sub)totals of rows and/or columns, the xls files contain corresponding control rows and control columns. See, for example, Figure  3 : if a check value is not equal to 0, it indicates an inconsistency in the table. That indicates either an error in the data entry or an error in the original material. A user of the dataset can find the cause of the inconsistency by comparing the original scan with the output files. The presence of the control counts is a valuable addition to other census datasets. Not only are control counts useful to end-users, but we also believe they should be part of census data by default. In this way, the data entry can be validated more easily, which benefits the scientific character of the dataset.
3. 2 Post-Processing The overall goal was to publish a fair dataset -Findable, Accessible, Interoperable, and Reusable. Publishing a dataset in easy contributes a lot to the first three aspects of FAIRness. For post-processing the data, the focus was on Reusability. This requires transparent procedures to enable reproducible results with a clear meaning for later analysis. To promote transparency, the file names of the images represented the names of the respecting municipalities. Following the instructions, data entry should produce Excel files with filenames conforming to the names of the images. We have given much effort to checking and correcting that relation between images and Excel files.
3. 2. 3 Assigning Version Numbers to Output Files The final step of post-processing was to assign version numbers to the output files. The 33,034 unique output files received the version number V0 if they were unique for the corresponding image. Otherwise, they received version number V1. For the V1 files, companion files got version numbers V2, etc. For end-users of the dataset, only the V0 and V1 versions are accessible. V1 files indicate that an earlier version is available at dans if users would want to check the version history. The output files are consistently available in Excel and in csv format.
3. 2. 4 Post-Processing -a Procedure and a Method of Documentation The process of post-processing illustrates the tedious attempt to get grip on this large dataset. By repeated sorting, counting, and comparison between extensive file lists, we searched for unexpected patterns in the sets of files. If we found any "inconsistencies", they have been corrected. The results of the corrections were checked by a systematic comparison of file lists "before" and "after" correction. Reproducibility of such a process requires adequate documentation, of course. The overall process is described in a series of tables (Excel workbooks) with accompanying documentation. The documentation describes the composition of the tables in the Excel workbooks step by step, worksheet by worksheet. Corrections concerning a moderate number of files were executed interactively and documented in the relevant Excel workbook.
3. 3. 1 Choosing it Tools As mentioned before, the main problem in dealing with the detailed census data of 1947 was the considerable number of files. Therefore, applications must be used that execute fast. Moreover, they must be easily transferable between people, remaining affordable, and without a need for extensive training. In addition, the applications must work transparently, to enable correct interpretation of the results of each operation. Finally, the application must support proper documentation of the operations executed. Given these requirements, we chose for ms Office (Excel with corresponding documentation in Word), ms-batch-scripts (home-made) and the Excel Converter utility by Svelte (2017). In the phase of uploading files to the dans archive we used asap Utilities (2018) to convert accented characters, as described below.
3. 3. 2 Designing the Final Structure of the Dataset When designing the data structure for the present dataset, the focus was on dissemination. For this purpose, the dataset was divided into 12 subsets, one dataset for each province of the Netherlands, plus one for the country as a whole. Using this structure, the number of files of the largest subset was brought down to about 17,000. The numbers of data files of this order of magnitude are a challenge for many repository systems, including easy. As the time needed to display a dataset in easy is proportional to its size, lowering the number of files per dataset provides a significant advantage to end-users. An even lower number of files per dataset could be reached by creating a subset per municipality, but as the Netherlands had more than a thousand municipalities in 1947, this method would not be feasible.
3. 3. 3 Uploading Datafiles and Metadata to the Digital Repository After the post-processing was completed and the data structure of the dataset had been designed, the data were prepared for uploading to the digital repository. First, the data for each subset per province was grouped into folders for the scans and the output files, respectively. Each dataset also contains a folder with a descriptive document and additional metadata regarding the table structures, the relation between scans and tables, and the templates used for data entry. Next, the data was uploaded to the repository. We did not run into major difficulties when uploading the data itself, despite the large number of files. In a third step, the metadata was uploaded. During this step, however, several errors did occur. It turned out that easy does not accept accented characters in its xml-based metadata, so we had to replace accented characters with detailed tables from the dutch census | 10.
4. 1 Using the Dataset for Historical Analysis As indicated above in section 2, the detailed dataset offers extensive possibilities for statistical historical research about the situation in the Netherlands after the end of World War ii. As an example, we consider the part of the population that did not have the Dutch nationality. The year 1947 is well before substantive international migration streams, both as a consequence of decolonization (from Indonesia, the former Dutch Indies; and later from Surinam and the Antilles), economic needs (guest labor), and political conditions (refugees, asylum seekers) elsewhere, began. In part A1 of the Census publications, Table  5  shows a relatively large part of Germans and Belgians, but also quite some people without nationality or with nationality unknown.
4. 2 Perspectives to Expand Usage of the Dataset Several features of the dataset support further analyses: the extensive documentation, the presence of control counts, and last but not least the availability of the output in tabular csv-text format. We mention three directions for further usage of the dataset. a. In the previous section, we mentioned two preparatory steps before we computed the ratios of people without Dutch nationality. First, we checked for inconsistencies in the tables. Next, we combined the files per municipality into one comprehensive file for the table considered. To enhance the reusability of the data, one could perform these steps in advance and publish the results. b. Another way to reuse the data is from the perspective of linked data. The analytic potential of linked data created from the published census tables is described in Meroño-Peñuela (2016),  Meroño-Peñuela et al.
(2017), and Ashkpour (2019). The organization of the dataset of the Population Census 1947 enables efficient preparation for the conversion to rdf for linking the data to other censuses and related population data. c. In addition to analyzing the rich content of the dataset of the 1947 census, the dataset gives a possibility to evaluate the potential of advanced ocr tools as an alternative to data entry. Applying new ocr methods to selected images in the present dataset will enable validation of those methods. Subsequently, the positively validated ocr tools could be used for the content conversion of the many other scanned images of detailed tabular data that are available in the dans repository.
Conclusion Historians and social scientists use many different types of sources to help them create an image of society, contemporary and in the past. Historical censuses are a special category, as they allow for the detailed study of the population over time. Our dataset with detailed tables of the Population Census 1947 forms such a source for the Netherlands shortly after World War ii. In retrospect, we think that this dataset is valuable in several ways. As we illustrated in section 4. 1, the dataset enables historical analyses on a detailed level of municipalities or even on the sub-municipal level. Further, by providing extensive documentation and presenting the datafiles in appropriate formats, the dataset enables (preparation for) applied analysis in other environments, for example in the context of linked data.
In the BullSequana XH2000 there is redundancy at the level of power, cooling, management and high-speed interconnect. For power redundancy, in the XH2000 design the AC/DC conversion within a rack is shared for all resource elements (compute node, switch, hydraulic chassis, etc. ). The power section is composed of a power distribution unit (PDU), power supply unit (PSU) shelves, optional ultracapacity module (UCM) and a busbar to distribute power to all the components within the cabinet. The level and type of redundancy is configurable for the PSU shelves, it could be configured at the PSU block or at the PSU shelf level with the following types of configuration: N (no redundancy), N þ 1, N þ 2, and 2N redundant. The optional UCM chassis allows the mitigation of micro power outages up to 300 ms at full load when three-phase uninterruptible power supply equipment for system shutdown is not present upstream in the data centre infrastructure.
Application-level resilience Application-level resilience techniques take a softwarebased approach, often with the focus on handling hard faults that result in the complete loss of one or more MPI processes. The objective is protecting the entire application from permanent failures in the computing resources and enabling it to continue, potentially on a reduced set of resources. As a consequence, application-level resilience may target specific types of software using approaches which cater to a subset of software. The most basic form of resilience is replication, whereby calculations are performed in duplicate on multiple nodes. This allows immediate continuation of the calculation in the event of a failure, but with a very inefficient use of resources during normal forward-path execution.
User-level failure mitigation and advanced checkpointing. Rather than forcing a restart of the entire application, localized mitigation of the failure is preferable. One particular challenge in the context of MPI applications has been the difficulty in recovering MPI communication in the event of hard faults. User-level failure mitigation (ULFM) extensions to the MPI standard provide an application-driven mechanism to detect and recover communication channels in the event of a hard fault leading to the loss of one or more processes. These extensions are currently under consideration for inclusion in the MPI 4. x standard  (Losada et al. , 2020). In prior versions of MPI, communication routines would either trigger an immediate abortion of the program or return control to the application to support a more controlled termination.
Mitigation of hard failures and implementation of application-level resilience techniques generally requires modification of existing codes to varying degrees. A number of libraries and packages already exist to facilitate this effort. Their impact ranges from almost zero intrusion through to a complete rewriting of the source code to incorporate resilience. ACR  (Ni et al. , 2013)  implements Automatic Checkpoint/Restart using replication of processes in order to handle both soft and hard errors. FA-MPI  (Hassani et al. , 2014)  proposes non-blocking transactional operations as an extension to the MPI standard to provide scalable failure detection, mitigation and recovery. Finally, FT-MPI  (Fagg and Dongarra, 2000)  was a precursor to ULFM for adding fault tolerance to the MPI 1. 2 standard.