Files to run the small dataset experiments used in the preprint "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available here. This .csv files enables to generate balanced small dataset from the PASTIS dataset. These files are required to run the experiment with a small training data-set, from the open source code ssl_ubarn. In the .csv file name selected_patches_fold_{FOLD}_nb_{NSITS}_seed_{SEED}.csv :

• FOLD: id which corresponds to one of the 5 experiments run due to PASTIS K-fold.
• NSITS: Number of SITS selected to construct this training data-set
• SEED: the randomness used to create this small dataset

This is a test collection for passage and document retrieval, produced in the TREC 2023 Deep Learning track. The Deep Learning Track studies information retrieval in a large training data regime. This is the case where the number of training queries with at least one positive label is at least in the tens of thousands, if not hundreds of thousands or more. This corresponds to real-world scenarios such as training based on click logs and training based on labels from shallow pools (such as the pooling in the TREC Million Query Track or the evaluation of search engines based on early precision).Certain machine learning based methods, such as methods based on deep learning are known to require very large datasets for training. Lack of such large scale datasets has been a limitation for developing such methods for common information retrieval tasks, such as document ranking. The Deep Learning Track organized in the previous years aimed at providing large scale datasets to TREC, and create a focused research effort with a rigorous blind evaluation of ranker for the passage ranking and document ranking tasks.Similar to the previous years, one of the main goals of the track in 2022 is to study what methods work best when a large amount of training data is available. For example, do the same methods that work on small data also work on large data? How much do methods improve when given more training data? What external data and models can be brought in to bear in this scenario, and how useful is it to combine full supervision with other forms of supervision?The collection contains 12 million web pages, 138 million passages from those web pages, search queries, and relevance judgments for the queries.

Dataset from Ngee Ann Polytechnic. For more information, visit https://data.gov.sg/datasets/d_d8fcd476d877512a2fff54c3fb55fc3f/view

List of Short Courses that TAFE SA offers as of 20th May 2015, Semester 2 2015

Federated Learning Solutions Market size was valued at USD 151.03 Million in 2024 and is projected to reach USD 292.47 Million by 2031, growing at a CAGR of 9.50% from 2024 to 2031.

Global Federated Learning Solutions Market Drivers

The market drivers for the Federated Learning Solutions Market can be influenced by various factors. These may include:

Data privacy worries are becoming more and more of a concern. Federated learning provides a mechanism to train machine learning models without gathering sensitive data centrally, which makes it a desirable solution for companies and organizations.
Data Security: Federated learning makes it possible for data to stay on local devices, lowering the possibility of data breaches and guaranteeing data security, which is essential for sectors like healthcare and finance that handle sensitive data.
Cost-Effectiveness: Federated learning can save organizations money by reducing the requirement for large-scale centralized infrastructure by dispersing the training process to local devices.
Regulatory Compliance: By keeping data local and minimizing data transfer, federated learning offers a solution for enterprises to comply with increasingly strict data protection rules, such as GDPR and HIPAA.
Edge Computing: By enabling model training directly on edge devices, edge computing—where data processing is done closer to the source of data—has boosted the viability and efficiency of federated learning.
Industry Adoption: To capitalize on the advantages of machine learning while resolving privacy and security concerns, a number of businesses, including healthcare, banking, and telecommunications, are progressively implementing federated learning solutions.
Technological developments in AI and ML: Federated learning has become a viable method for training models on dispersed data sources as AI and ML technologies develop, spurring additional market innovation and uptake.

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a “Second Solubility Challenge” in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets.

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US Compliance Training For Financial Institutions Market Size 2025-2029

The US compliance training for financial institutions market size is forecast to increase by USD 1.6 billion at a CAGR of 14.7% between 2024 and 2029.

The market is witnessing significant growth due to the increasing need for skilled employees who can adhere to regulatory requirements. Another trend driving market growth is the popularity of learning analytics, which enables organizations to track and measure the effectiveness of their training programs. Open-source training platforms are also gaining traction, offering cost-effective solutions for financial institutions. These platforms provide flexibility and customization options, making it easier for organizations to tailor their training programs to specific regulatory needs. The use of technology, such as artificial intelligence and machine learning, is also transforming training by automating processes and delivering personalized learning experiences. However, challenges such as keeping up with evolving regulations and ensuring data security remain key concerns for financial institutions. Overall, the market is expected to continue growing as organizations prioritize training to mitigate risks and ensure regulatory compliance.

What will be the Size of the market During the Forecast Period?

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The market continues to experience significant growth due to the increasing importance of rules and regulations In the sector. With the expanding use of internet infrastructure and smart devices, the need for strong information security training and regulatory compliance training has become essential for both small and medium enterprises and large organizations. In response, technological improvement in areas such as cloud computing and learning analytics technologies have emerged as long-term solutions for delivering effective training programs. Sexual harassment training, code-of-conduct and ethics, and cyber security training are among the key compliance areas prioritized by financial institutions.
Partnership strategies between training providers and financial institutions are also on the rise, enabling organizations to address their unique compliance needs. The market's size and direction reflect the evolving business landscape, with a focus on maintaining a secure and compliant workforce in an increasingly complex regulatory environment.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Courses

  Professional courses
  Introductory courses


Delivery Mode

  Offline learning
  Online learning


Geography

  US

By Courses Insights

The professional courses segment is estimated to witness significant growth during the forecast period. The market is experiencing substantial growth due to the increasing importance of employee training in adhering to complex rules and regulations. E-learning solutions, facilitated by the internet infrastructure and smart devices, have become essential for small and medium enterprises as well as large organizations. Technological improvements and changing business needs have led to the adoption of SMAC technology and online training solutions. Compliance training covers various areas, including information security, regulatory compliance, sexual harassment, code-of-conduct and ethics, cyber security, and diversity. Market leaders such as Cornerstone offer comprehensive training programs. Driving factors include the need for continuous monitoring strategies, legacy systems, and the increasing complexity of businesses.

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Market Dynamics

Our market researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adoption of US Compliance Training For Financial Institutions Market?

The rising need for skilled employees is the key driver of the market. The financial services industry In the US is experiencing a shift in workforce dynamics, with an increasing reliance on external talent and the need for continuous skill development among employees. This trend is driven by changing business needs and technological improvements, including the adoption of SMAC technology, cloud computing, and centralized data storage. It is a critical area of focus for financial institutions, given the complex regulatory landscape and the potential risks associated with non-compliance. Online training solutions, such as e-learning and intera

The data provided here are part of a Galaxy Training Network tutorial that analyzes small RNA-seq (sRNA-seq) data from a study published by Harrington et al. (DOI:10.1186/s12864-017-3692-8) to detect differential abundance of various classes of endogenous short interfering RNAs (esiRNAs). The goal of this study was to investigate "connections between differential retroTn and hp-derived esiRNA processing and cellular location, and to investigate the potential link between mRNA 3' end cleavage and esiRNA biogenesis." To this end, sRNA-seq libraries were constructed from triplicate Drosophila tissue culture samples under conditions of either control RNAi or RNAi knockdown of a factor involved in mRNA 3' end processing, Symplekin. This dataset (GEO Accession: GSE82128) consists of single-end, size-selected, non-rRNA-depleted sRNA-seq libraries. Because of the long processing time for the large original files, we have downsampled the original raw data files to include only reads that align to a subset of interesting transcript features including: (1) transposable elements, (2) Drosophila piRNA clusters, (3) Symplekin, and (4) genes encoding mass spectrometry-defined protein binding partners of Symplekin from Additional File 2 in the indicated paper by Harrington et al. More details on features 1 and 2 can be found here: https://github.com/bowhan/piPipes/blob/master/common/dm3/genomic_features (piRNA_Cluster, Trn). All features are from the Drosophila genome Apr. 2006 (BDGP R5/dm3) release.

Short course series is a book series. It includes 7 books, written by 6 different authors.

Listing of vocational training courses eligible for Individual Training Grants

The website of the Small and Medium Enterprise Internet University provides information on online learning courses.

This is a dataset updated annually the description below relates to the first year of online release, since updates have taken place in 2018 (data 2008-2017) and 2019 (data 2009-2018). Paris 13 University recorded data on student registration in its information system (Apogee software) for each academic year between 2006(-2007) and 2015(-2016). These data relate to the diplomas prepared, the steps to achieve this, the scheme (if it concerns initial training or apprenticeship), the relevant components (UFR, IUT, etc.), and the origin of students (type of baccalaureate, academy of origin, nationality). Each entry concerns the main enrollment of a student at the university for a year. The attributes of this data are as follows. — CODE_INDIVIDU Hidden Data — ANNEE_INSCRIPTION Year of registration:2006 for 2006-2007, etc. — LIB_DIPLOME Diploma Name — LEVEAU_DANS_LE_DIPLOME 1, 2,... for master 1, license 2, etc. — LEVEAU_APRES_BAC 1, 2,... for Bac+ 1, Bac+ 2,... — LIBELLE_DISCIPLINE_DIPLOME Attachment of the diploma to a discipline — CODE_SISE_DIPLOME Student Tracking Information System Code — CODE_ETAPE Internal code of a stage (year, course) of diploma — LIBELLE_COURT_ETAPE Short name of step — LIBELLE_LONG_ETAPE More intelligible name of the step — LIBELLE_COURT_COMPOSANT Name of component (UFR, IUT etc.) — CODE_COMPOSANT Number code of component (unused) — REGROUPEMENT_BAC Type of Bac (L, ES, S, techno STMG, techno ST2S,...) — LIBELLE_ACADEMIE_BAC Academy of Bac (Creteil, Versailles, foreigner,...) — Continent Deduced of nationality which is masked data — LIBELLE_REGIME Initial training, continuing, pro, learning Paris 13 University publishes part of this dataset through several resources, while respecting the anonymity of its students. Starting from 213,289 entries that correspond to all enrolments of the 106,088 individuals who studied at Paris 13 University during the ten academic years between 2006(2007) and 2015(-2016), we selected several resources each corresponding to a part of the data. To produce each resource we chose a small number of attributes, then removed a small proportion of the inputs, in order to satisfy a k-anonymisation constraint with k = 5, i.e. to ensure that, in each resource, each entry appears at least 5 times identical (otherwise the input is deleted). The four resources produced are materialised by the following files. — The file ‘up13_etapes.csv’ concerns the diploma steps, it contains the attributes “CODE_ETAPE”, “LIBELLE_COURT_ETAPE”, “LIBELLE_LONG_ETAPE”, “NIVEAU_APRES_BAC”, “LIBELLE_COURT_COMPOSANTE”, “LIBELLE_DISCIPLINE_DIPLOME”, “CODE_SISE_DIPLOME”, “NIVEAU_DANS_LE_DIPLOME” and its anonymisation causes a loss of 918 entries. — The file ‘up13_Academie.csv’ concerns the Bac Academy and it contains the attributes “LIBELLE_ACADEMIE_BAC”, “NIVEAU_APRES_BAC”, “NIVEAU_DANS_DIPLOME”, “CONTINENT”, “LIBELLE_REGIME”, “LIB_DIPLOME”, “LIBELLE_COURT_COMPOSANTE” and its anoymisation causes the loss of 7525 entries. — The file ‘up13_Bac.csv’ concerns the type of Bac and the level reached after the Bac, it contains the columns “REGROUPEMENT_BAC”, “NIVEAU_APRES_BAC”, “LIBELLE_REGIME”, “CONTINENT”, “LIBELLE_COURT_COMPOSANTE”, “LIB_DIPLOME”, “NIVEAU_DANS_LE_DIPLOME” and its anonymisation causes the loss of 3,933 entries. — The file ‘up13_annees_etapes.csv’ concerns enrolment in the diploma stages year after year, it contains the columns “ANNEE_INSCRIPTION”, “LIBELLE_COURT_COMPOSANTE”, “NIVEAU_APRES_BAC”, “LIB_DIPLOME”, “CODE_ETAPE” and its anonymisation causes the loss of 3,532 entries. Other tables extracted from the same initial data and constructed using the same method of anonymisation can be provided on request (specify the desired columns). A second set of resources offers the follow-up of students year after year, from degree stage to degree stage. In this dataset, we call trace such tracking when the registration year has been forgotten and only the sequence remains. And we call cursus a data describing this succession of steps over the years. For anonymisation we have grouped the traces or the same paths and as soon as there were less than 10 we do not indicate their number, or, what amounts to the same, we put this number to 1 (the information being that there is at least one student who left this trace or followed this course). This leads to forgetting a number of too specific study paths and keeping only one as a witness. Starting from 106,088 trails or tracks, we produce the following resources. — The file ‘up13_traces.csv’ contains the sequence of diploma step codes (a trace) and anonymisation makes us forget 10 089 traces. — The file ‘up13_traces_wt_etape.csv’ contains similar traces, but without the step code. That is to say, only the diploma, the level after baccalaureate and the component concerned remain. Anonymisation makes us forget 4,447 traces. — The file ‘up13_traces_bac_wt_etape.csv’ contains the same data as in the file ‘up13_traces_wt_etape.csv’ but also with the Bac type. Anonymisation makes us forget 8,067 traces. — The file ‘up13_cursus_wt_etape.csv’ contains the same data as in the file ‘up13_traces_wt_etape.csv’ with the additional registration years. Anonymisation makes us forget 8,324 courses.

The Secretary of State designates these courses as eligible for tuition fee loans under the Higher Education Short Course Loans Regulations 2022. This is under the powers of The Teaching and Higher Education Act 1998, section 22.

Information for HESC learners is available.

SmolLM-Corpus

This dataset is a curated collection of high-quality educational and synthetic data designed for training small language models. You can find more details about the models trained on this dataset in our SmolLM blog post.

  Dataset subsets







  Cosmopedia v2

Cosmopedia v2 is an enhanced version of Cosmopedia, the largest synthetic dataset for pre-training, consisting of over 39 million textbooks, blog posts, and stories generated by… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus.

Nanoparticles exhibit broad applications in materials mechanics, medicine, energy and other fields. The ordered arrangement of nanoparticles is very important to fully understand their properties and functionalities. However, in materials science, the acquisition of training images requires a large number of professionals and the labor cost is extremely high, so there are usually very few training samples in the field of materials. In this study, a segmentation method of nanoparticle topological structure based on synthetic data (SD) is proposed, which aims to solve the issue of small data in the field of materials. Our findings reveal that the combination of SD generated by rendering software with merely 15% Authentic Data (AD) shows better performance in training deep learning model. The trained U-Net model shows that Miou of 0.8476, accuracy of 0.9970, Kappa of 0.8207, and Dice of 0.9103, respectively. Compared with data enhancement alone, our approach yields a 1% improvement in the Miou metric. These results show that our proposed strategy can achieve better prediction performance without increasing the cost of data acquisition.

Additional information reported in lieu of inclusion in the annual report. To read the complete annual report visit www.desbt.qld.gov.au and search for 'annual report'.

This dataset is about books and is filtered where the book is Assessing the need for short courses in library/information work, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

StatBank dataset: FOHOJ03B Title: High school students on short courses by gender, age, personal income, unit and time indication Period type: years Period format (time in data): yyyy The oldest period: 2016 The most recent period: 2023

Additional information reported in lieu of inclusion in the annual report. To read the complete annual report, visit www.desbt.qld.gov.au and search for 'annual report'.

As there was no large publicly available cross-domain dataset for comparative argument mining, we create one composed of sentences, potentially annotated with BETTER / WORSE markers (the first object is better / worse than the second object) or NONE (the sentence does not contain a comparison of the target objects). The BETTER sentences stand for a pro-argument in favor of the first compared object and WORSE-sentences represent a con-argument and favor the second object. We aim for minimizing dataset domain-specific biases in order to capture the nature of comparison and not the nature of the particular domains, thus decided to control the specificity of domains by the selection of comparison targets. We hypothesized and could confirm in preliminary experiments that comparison targets usually have a common hypernym (i.e., are instances of the same class), which we utilized for selection of the compared objects pairs. The most specific domain we choose, is computer science with comparison targets like programming languages, database products and technology standards such as Bluetooth or Ethernet. Many computer science concepts can be compared objectively (e.g., on transmission speed or suitability for certain applications). The objects for this domain were manually extracted from List of-articles at Wikipedia. In the annotation process, annotators were asked to only label sentences from this domain if they had some basic knowledge in computer science. The second, broader domain is brands. It contains objects of different types (e.g., cars, electronics, and food). As brands are present in everyday life, anyone should be able to label the majority of sentences containing well-known brands such as Coca-Cola or Mercedes. Again, targets for this domain were manually extracted from `List of''-articles at Wikipedia.The third domain is not restricted to any topic: random. For each of 24~randomly selected seed words 10 similar words were collected based on the distributional similarity API of JoBimText (http://www.jobimtext.org). Seed words created using randomlists.com: book, car, carpenter, cellphone, Christmas, coffee, cork, Florida, hamster, hiking, Hoover, Metallica, NBC, Netflix, ninja, pencil, salad, soccer, Starbucks, sword, Tolkien, wine, wood, XBox, Yale.Especially for brands and computer science, the resulting object lists were large (4493 in brands and 1339 in computer science). In a manual inspection, low-frequency and ambiguous objects were removed from all object lists (e.g., RAID (a hardware concept) and Unity (a game engine) are also regularly used nouns). The remaining objects were combined to pairs. For each object type (seed Wikipedia list page or the seed word), all possible combinations were created. These pairs were then used to find sentences containing both objects. The aforementioned approaches to selecting compared objects pairs tend minimize inclusion of the domain specific data, but do not solve the problem fully though. We keep open a question of extending dataset with diverse object pairs including abstract concepts for future work. As for the sentence mining, we used the publicly available index of dependency-parsed sentences from the Common Crawl corpus containing over 14 billion English sentences filtered for duplicates. This index was queried for sentences containing both objects of each pair. For 90% of the pairs, we also added comparative cue words (better, easier, faster, nicer, wiser, cooler, decent, safer, superior, solid, terrific, worse, harder, slower, poorly, uglier, poorer, lousy, nastier, inferior, mediocre) to the query in order to bias the selection towards comparisons but at the same time admit comparisons that do not contain any of the anticipated cues. This was necessary as a random sampling would have resulted in only a very tiny fraction of comparisons. Note that even sentences containing a cue word do not necessarily express a comparison between the desired targets (dog vs. cat: He's the best pet that you can get, better than a dog or cat.). It is thus especially crucial to enable a classifier to learn not to rely on the existence of clue words only (very likely in a random sample of sentences with very few comparisons). For our corpus, we keep pairs with at least 100 retrieved sentences.From all sentences of those pairs, 2500 for each category were randomly sampled as candidates for a crowdsourced annotation that we conducted on figure-eight.com in several small batches. Each sentence was annotated by at least five trusted workers. We ranked annotations by confidence, which is the figure-eight internal measure of combining annotator trust and voting, and discarded annotations with a confidence below 50%. Of all annotated items, 71% received unanimous votes and for over 85% at least 4 out of 5 workers agreed -- rendering the collection procedure aimed at ease of annotation successful.The final dataset contains 7199 sentences with 271 distinct object pairs. The majority of sentences (over 72%) are non-comparative despite biasing the selection with cue words; in 70% of the comparative sentences, the favored target is named first.You can browse though the data here: https://docs.google.com/spreadsheets/d/1U8i6EU9GUKmHdPnfwXEuBxi0h3aiRCLPRC-3c9ROiOE/edit?usp=sharing Full description of the dataset is available in the workshop paper at ACL 2019 conference. Please cite this paper if you use the data: Franzek, Mirco, Alexander Panchenko, and Chris Biemann. ""Categorization of Comparative Sentences for Argument Mining."" arXiv preprint arXiv:1809.06152 (2018).@inproceedings{franzek2018categorization, title={Categorization of Comparative Sentences for Argument Mining}, author={Panchenko, Alexander and Bondarenko, and Franzek, Mirco and Hagen, Matthias and Biemann, Chris}, booktitle={Proceedings of the 6th Workshop on Argument Mining at ACL'2019}, year={2019}, address={Florence, Italy}}