dataesr/scientific-paragraphs-categorization huggingface dataset:
The ETh-80 dataset includes 80 3D objects in eight different object categories including apple, car, toy cow, cup, toy dog, toy horse, pear, and tomato. Each object is photographed in 41 viewpoints with different view angles and different tilts. Figure  S1  in Supplementary Information provides some examples of objects in this dataset from different viewpoints. For both datasets, five instances of each object category are selected for the training set to be used in the learning phase. The remaining instances constitute the testing set which is not seen during the learning phase, but is used afterward to evaluate the recognition performance. This standard cross-validation procedure allows to measure the generalization ability of the model beyond the specific training examples. Note that for 3D-Object dataset, the original size of all images were preserved, while the images of ETH-80 dataset are resized to 300 pixels in height while preserving the aspect ratio.
The images of both datasets were converted to grayscale values. As already mentioned, the building process of S 2 features is performed in a completely unsupervised manner. Hence, through the execution of the unsupervised STDP-based learning, the training images are randomly fed into the model (without considering their class labels, viewpoints, scales, and tilts). The learning process starts with initial random weights and finishes when 600 spikes have occurred in each S 2 map. Then STDP is turned off, and the ability of the obtained features to invariantly represent different object classes is evaluated. To compute the corresponding C 2 feature vector for each input image, the thresholds of C 2 neurons are set to infinite, and their final potentials are evaluated, after propagating the whole spike train generated by each image.
We have used a pre-trained DeepConvNet model implemented by Jia, et al. [42] , whose code is also available at http://caffe. berkeleyvision. org. The training was done over the ILSVRC2012 dataset (a subset of ImageNet) with about 1. 2 million images in 1000 categories. We fed the training and testing images into DeepConvNet and extracted the feature values from the 7th layer. Again, a SVM is used to do the object recognition based on the extracted features.
Performance analysis Table  1  provides the accuracy of our model in category classification independently of view, tilt, and scale, when different number of S 2 features are learned by the STDP-based learning algorithm. The results indicate that the model reaches a high classification performance on 3D-Object dataset with about 300 C 2 features only (about 30 features per class). The performance is flattened around 96% for feature vectors of size greater than 400. Also, for the ETH-80 dataset, the model attains to a reasonable recognition accuracy of about 81% with only 1250 extracted features. We have also performed the same experiments on HMAX and DeepConvNet models which their accuracies are also provided in Table  1. Performance of the HMAX model was weak on both datasets, which is not too surprising, several previous studies have shown that the performance of the HMAX model extensively decreases when facing significant object transformations  [28, 29].
Given the structural similarities between our model and HMAX, the superiority of our model is presumably related to the unsupervised feature learning. Indeed, most of the randomly extracted S2 patches in HMAX tend to be redundant and irrelevant, as we will see in the next section. DeepConvNet reached a mean performance of about 86% on 3D-Object and about 79% on ETH-80 dataset. Thus, our model outperforms Deep-ConvNet on both datasets, which itself significantly outperforms HMAX. It should be noted that the images of each object in these two datasets are highly varied (e. g. , in 3D-Object dataset, there is a 45 • difference between two successive views of an object) and it has previously been shown that the performance of DeepConvNet drops when facing such transformations  [30]. Another drawback of DeepConvNet is that, due to the large number of parameters, it needs to be trained over millions of images to avoid overfitting  [43]  (here we avoided this problem by using a pre-trained version, but doing the training on about 3500 images we used here would presumably lead to massive overfitting).
Feature Analysis In this section, we demonstrate that unsupervised STDP learning algorithm extracts informative and diagnostic features by comparing them to the randomly picked HMAX features. To this end, we have used several feature analysis techniques: representational dissimilarity matrices, hierarchical clustering, and mutual information. We performed the same analyses on both datasets and obtained similar results. Hence, the results of ETH-80 are presented in Supplementary Information. Extraction of diagnostic features let our model reach high classification performances with a small number of features (c. f. Table  1 ). To understand why this is true, we first reconstructed the features' preferred stimuli. Given that each S 2 neuron receives spikes from C 1 neurons responding to bars in different orientations, the representation of the preferred features of S 2 neurons could be reconstructed by convolving their weight matrices with a set of kernels representing oriented bars.
In Fig. S2  the receptive fields of activated S 2 neurons along with the representation of their preferred stimuli are illustrated (Fig. S2  provides the same illustration for the ETH-80 dataset). This demonstrates that only a small number of S 2 neurons are required to represent the input objects. In other words, the obtained features are compatible with the sparse coding theory in visual cortex. In addition, for an input image, the most activated S 2 neurons cover the input objects and they do not respond to the background area. Indeed, the STDP learning algorithm naturally focuses on what are common in the training images, which are the target object features. The backgrounds are generally not learned (at least not in priority), since they are almost always too different from one image to another and the STDP process cannot converge on them.
Here, we show that the superiority of our model is due to the informative feature extraction through a bio-inspired learning rule. To this end, we have compared the performances on 3D-Object dataset obtained with random features versus STDP features, as well as a very simple classifier versus SVM. To generate random features, we have set the weight matrix of each S 2 feature of our model with random values. First, we have computed the mean and standard deviation (STD) (253 ± 21) of the number of active (nonzero) weights in the features learned by STDP. Second, for each random feature, the number of active weights, N , is computed by generating a random number based on the obtained mean and STD. Finally, a random feature is constructed by uniformly distributing the N randomly generated values in the weight matrix.
However, parallel implementation of our algorithm could speed-up the computation time by several orders of magnitude  [45]. Even in this case, we do not expect to outperform the DeepConvNet model on the ImageNet database, since only the shape similarities are taken into account in our model and the other cues such as color or texture are ignored. Importantly, our algorithm has a natural tendency to learn salient contrasted regions  [24] , which is desirable as these are typically the most informative  [46]. Most of our C 2 features turned out to be class-specific, and we could guess what they represent by doing the reconstructions (see Fig. S2  and Fig. S2 ). Since each feature results from averaging multiple input images, the specificity of each instance is averaged out, leading to class archetypes.
Conclusions To date, various bio-inspired network architectures for object recognition have been introduced, but the learning mechanism of biological visual systems has been neglected. In this paper, we demonstrate that the association of both bio-inspired network architecture and learning rule results in a robust object recognition system. The STDP-based feature learning, used in our model, extracts frequent diagnostic and class specific features that are robust to deformations in stimulus appearance. It has previously been shown that the trivial models can not tolerate the identity preserving transformations such as changes in view, scale, and position. To study the behavior of our model confronted with these difficulties, we have tested our model over two challenging invariant object recognition databases which includes instances of 10 different object classes photographed in different views, scales, and tilts.
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
concerned with long-term data stewardship; and a data science community mining, integrating and analysing new and existing data to advance discovery. To facilitate the reading of this manuscript by these diverse stakeholders, we provide definitions for common abbreviations in Box 1. Humans, however, are not the only critical stakeholders in the milieu of scientific data. Similar problems are encountered by the applications and computational agents that we task to undertake data retrieval and analysis on our behalf. These 'computational stakeholders' are increasingly relevant, and demand as much, or more, attention as their importance grows. One of the grand challenges of data-intensive science, therefore, is to improve knowledge discovery through assisting both humans, and their computational agents, in the discovery of, access to, and integration and analysis of, task-appropriate scientific data and other scholarly digital objects.
For certain types of important digital objects, there are well-curated, deeply-integrated, special-purpose repositories such as Genbank 3 , Worldwide Protein Data Bank (wwPDB 4 ), and UniProt 5 in the life sciences; Space Physics Data Facility (SPDF; http://spdf. gsfc. nasa. gov/) and Set of Identifications, Measurements and Bibliography for Astronomical Data (SIMBAD  6  ) in the space sciences. These foundational and critical core resources are continuously curating and capturing highvalue reference datasets and fine-tuning them to enhance scholarly output, provide support for both human and mechanical users, and provide extensive tooling to access their content in rich, dynamic ways. However, not all datasets or even data types can be captured by, or submitted to, these repositories. Many important datasets emerging from traditional, low-throughput bench science don't fit in the data models of these special-purpose repositories, yet these datasets are no less important with respect to integrative research, reproducibility, and reuse in general.
Apparently in response to this, we see the emergence of numerous general-purpose data repositories, at scales ranging from institutional (for example, a single university), to open globally-scoped repositories such as Dataverse  7  , FigShare (http://figshare. com), Dryad 8 , Mendeley Data (https://data. mendeley. com/), Zenodo (http:// zenodo. org/), DataHub (http://datahub. io), DANS (http://www. dans. knaw. nl/), and EUDat  9. Such repositories accept a wide range of data types in a wide variety of formats, generally do not attempt to integrate or harmonize the deposited data, and place few restrictions (or requirements) on the descriptors of the data deposition. The resulting data ecosystem, therefore, appears to be moving away from centralization, is becoming more diverse, and less integrated, thereby exacerbating the discovery and re-usability problem for both human and computational stakeholders.
A specific example of these obstacles could be imagined in the domain of gene regulation and expression analysis. Suppose a researcher has generated a dataset of differentially-selected polyadenylation sites in a non-model pathogenic organism grown under a variety of environmental conditions that stimulate its pathogenic state. The researcher is interested in comparing the alternatively-polyadenylated genes in this local dataset, to other examples of alternative-polyadenylation, and the expression levels of these genes-both in this organism and related model organisms-during the infection process. Given that there is no special-purpose archive for differential polyadenylation data, and no model organism database for this pathogen, where does the researcher begin? We will consider the current approach to this problem from a variety of data discovery and integration perspectives.
If the desired datasets existed, where might they have been published, and how would one begin to search for them, using what search tools? The desired search would need to filter based on specific species, specific tissues, specific types of data (Poly-A, microarray, NGS), specific conditions (infection), and specific genes-is that information ('metadata') captured by the repositories, and if so, what formats is it in, is it searchable, and how? Once the data is discovered, can it be downloaded? In what format(s)? Can that format be easily integrated with private in-house data (the local dataset of alternative polyadenylation sites) as well as other data publications from third-parties and with the community's core gene/protein data repositories? Can this integration be Box 1 | Terms and Abbreviations BD2K-Big Data 2 Knowledge, is a trans-NIH initiative established to enable biomedical research as a digital research enterprise, to facilitate discovery and support new knowledge, and to maximise community engagement.
DOI-Digital Object Identifier; a code used to permanently and stably identify (usually digital) objects. DOIs provide a standard mechanism for retrieval of metadata about the object, and generally a means to access the data object itself. FAIR-Findable, Accessible, Interoperable, Reusable. FORCE11-The Future of Research Communications and e-Scholarship; a community of scholars, librarians, archivists, publishers and research funders that has arisen organically to help facilitate the change toward improved knowledge creation and sharing, initiated in 2011. Interoperability-the ability of data or tools from non-cooperating resources to integrate or work together with minimal effort. JDDCP-Joint Declaration of Data Citation Principles; Acknowledging data as a first-class research output, and to support good research practices around data re-use, JDDCP proposes a set of guiding principles for citation of data within scholarly literature, another dataset, or any other research object.
Overcoming these barriers, therefore, necessitates that all stakeholders-including researchers, special-purpose, and general-purpose repositories-evolve to meet the emergent challenges described above. The goal is for scholarly digital objects of all kinds to become 'first class citizens' in the scientific publication ecosystem, where the quality of the publication-and more importantly, the impact of the publication-is a function of its ability to be accurately and appropriately found, reused, and cited over time, by all stakeholders, both human and mechanical. With this goal in-mind, a workshop was held in Leiden, Netherlands, in 2014, named 'Jointly Designing a Data Fairport'. This workshop brought together a wide group of academic and private stakeholders all of whom had an interest in overcoming data discovery and reuse obstacles.
The significance of machines in data-rich research environments The emphasis placed on FAIRness being applied to both human-driven and machine-driven activities, is a specific focus of the FAIR Guiding Principles that distinguishes them from many peer initiatives (discussed in the subsequent section). Humans and machines often face distinct barriers when attempting to find and process data on the Web. Humans have an intuitive sense of 'semantics' (the meaning or intent of a digital object) because we are capable of identifying and interpreting a wide variety of contextual cues, whether those take the form of structural/visual/iconic cues in the layout of a Web page, or the content of narrative notes. As such, we are less likely to make errors in the selection of appropriate data or other digital objects, although humans will face similar difficulties if sufficient contextual metadata is lacking.
The primary limitation of humans, however, is that we are unable to operate at the scope, scale, and speed necessitated by the scale of contemporary scientific data and complexity of e-Science. It is for this reason that humans increasingly rely on computational agents to undertake discovery and integration tasks on their behalf. This necessitates machines to be capable of autonomously and appropriately acting when faced with the wide range of types, formats, and access-mechanisms/protocols that will be encountered during their self-guided exploration of the global data ecosystem. It also necessitates that the machines keep an exquisite record of provenance such that the data they are collecting can be accurately and adequately cited. Assisting these agents, therefore, is a critical consideration for all participants in the data management and stewardship process-from researchers and data producers to data repository hosts.
The optimal state-where machines fully 'understand' and can autonomously and correctly operate-on a digital object-may rarely be achieved. Nevertheless, the FAIR principles provide 'steps along a path' toward machine-actionability; adopting, in whole or in part, the FAIR principles, leads the resource along the continuum towards this optimal state. In addition, the idea of being machine-actionable applies in two contexts-first, when referring to the contextual metadata surrounding a digital object ('what is it?'), and second, when referring to the content of the digital object itself ('how do I process it/integrate it?'). Either, or both of these may be machine-actionable, and each forms its own continuum of actionability. Finally, we wish to draw a distinction between data that is machine-actionable as a result of specific investment in software supporting that data-type, for example, bespoke parsers that understand life science wwPDB files or space science Space Physics Archive Search and Extract (SPASE) files, and data that is machine-actionable exclusively through the utilization of general-purpose, open technologies.
The FAIR Guiding Principles in detail Representatives of the interested stakeholder-groups, discussed above, coalesced around four core desiderata-the FAIR Guiding Principles-and limited elaboration of these, which have been refined (Box 2) from the meeting's original draft, available at (https://www. force11. org/node/6062). A separate document that dynamically addresses community discussion relating to clarifications and explanations of the principles, and detailed guidelines for and examples of FAIR implementations, is currently being constructed (http://datafairport. org/fair-principles-living-document-menu). The FAIR Guiding Principles describe distinct considerations for contemporary data publishing environments with respect to supporting both manual and automated deposition, exploration, sharing, and reuse.
While there have been a number of recent, often domain-focused publications advocating for specific improvements in practices relating to data management and archival  1, 11, 12  , FAIR differs in that it describes concise, domain-independent, high-level principles that can be applied to a wide range of scholarly outputs. Throughout the Principles, we use the phrase '(meta)data' in cases where the Principle should be applied to both metadata and data. The elements of the FAIR Principles are related, but independent and separable. The Principles define characteristics that contemporary data resources, tools, vocabularies and infrastructures should exhibit to assist discovery and reuse by third-parties. By minimally defining each guiding principle, the barrier-to-entry for data producers, publishers and stewards who wish to make their data holdings FAIR is purposely maintained as low as possible.
The Principles may be adhered to in any combination and incrementally, as data providers' publishing environments evolve to increasing degrees of 'FAIRness'. Moreover, the modularity of the Principles, and their distinction between data and metadata, explicitly support a wide range of special circumstances. One such example is highly sensitive or personally-identifiable data, where publication of rich metadata to facilitate discovery, including clear rules regarding the process for accessing the data, provides a high degree of 'FAIRness' even in the absence of FAIR publication of the data itself. A second example involves the publication.
I2. (meta)data use vocabularies that follow FAIR principles I3. (meta)data include qualified references to other (meta)data To be Reusable: R1. meta(data) are richly described with a plurality of accurate and relevant attributes R1. 1. (meta)data are released with a clear and accessible data usage license R1. 2. (meta)data are associated with detailed provenance R1. 3. (meta)data meet domain-relevant community standards of non-data research objects. Analytical workflows, for example, are a critical component of the scholarly ecosystem, and their formal publication is necessary to achieve both transparency and scientific reproducibility. The FAIR principles can equally be applied to these non-data assets, which need to be identified, described, discovered, and reused in much the same manner as data.
Examples of FAIRness, and the resulting value-added Dataverse 7 : Dataverse is an open-source data repository software installed in dozens of institutions globally to support public community repositories or institutional research data repositories. Harvard Dataverse, with more than 60,000 datasets, is the largest of the current Dataverse repositories, and is open to all researchers from all research fields. Dataverse generates a formal citation for each deposit, following the standard defined by Altman and King  13. Dataverse makes the Digital Object Identifier (DOI), or other persistent identifiers (Handles), public when the dataset is published ('F'). This resolves to a landing page, providing access to metadata, data files, dataset terms, waivers or licenses, and version information, all of which is indexed and searchable ('F', 'A', and 'R').
Deposits include metadata, data files, and any complementary files (such as documentation or code) needed to understand the data and analysis ('R'). Metadata is always public, even if the data are restricted or removed for privacy issues ('F', 'A'). This metadata is offered at three levels, extensively supporting the 'I' and 'R' FAIR principles: 1) data citation metadata, which maps to DataCite schema or Dublin Core Terms, 2) domain-specific metadata, which when possible maps to metadata standards used within a scientific domain, and 3) file-level metadata, which can be deep and extensive for tabular data files (including column-level metadata). Finally, Dataverse provides public machine-accessible interfaces to search the data, access the metadata and download the data files, using a token to grant access when data files are restricted ('A').