179,640 research outputs found
Statistical Physics
No full textThe Manchester Physics Series General Editors: D. J. Sandiford; F. Mandl; A. C. Phillips Department of Physics and Astronomy, University of Manchester Properties of Matter B. H. Flowers and E. Mendoza Optics Second Edition F. G. Smith and J. H. Thomson Statistical Physics Second Edition E. Mandl Electromagnetism Second Edition I. S. Grant and W. R. Phillips Statistics R. J. Barlow Solid State Physics Second Edition J. R. Hook and H. E. Hall Quantum Mechanics F. Mandl Particle Physics Second Edition B. R. Martin and G. Shaw The Physics of Stars Second Edition A. C. Phillips Computing for Scien
Povezanost med hitrostjo vetra in dolžino poleta smučarja skakalca na poletih v Planici marca 2013
No full textQuantum mechanics
No full textThe Manchester Physics Series General Editors: D. J. Sandiford; F. Mandl; A. C. Phillips Department of Physics and Astronomy, University of Manchester Properties of Matter B. H. Flowers and E. Mendoza Optics Second Edition F. G. Smith and J. H. Thomson Statistical Physics Second Edition F. Mandl Electromagnetism Second Edition I. S. Grant and W. R. Phillips Statistics R. J. Barlow Solid State Physics Second Edition J. R. Hook and H. E. Hall Quantum Mechanics F. Mandl Particle Physics Second Edition B. R. Martin and G. Shaw The Physics of Stars Second Edition A. C. Phillips Computing for Scien
CT-FAN: A Multilingual dataset for Fake News Detection
No full textBy downloading the data, you agree with the terms & conditions mentioned below:
Data Access: The data in the research collection may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use them only for research purposes.
Summaries, analyses and interpretations of the linguistic properties of the information may be derived and published, provided it is impossible to reconstruct the information from these summaries. You may not try identifying the individuals whose texts are included in this dataset. You may not try to identify the original entry on the fact-checking site. You are not permitted to publish any portion of the dataset besides summary statistics or share it with anyone else.
We grant you the right to access the collection's content as described in this agreement. You may not otherwise make unauthorised commercial use of, reproduce, prepare derivative works, distribute copies, perform, or publicly display the collection or parts of it. You are responsible for keeping and storing the data in a way that others cannot access. The data is provided free of charge.
Citation
Please cite our work as
@InProceedings{clef-checkthat:2022:task3,
author = {K{\"o}hler, Juliane and Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Wiegand, Michael and Siegel, Melanie and Mandl, Thomas},
title = "Overview of the {CLEF}-2022 {CheckThat}! Lab Task 3 on Fake News Detection",
year = {2022},
booktitle = "Working Notes of CLEF 2022---Conference and Labs of the Evaluation Forum",
series = {CLEF~'2022},
address = {Bologna, Italy},}
@article{shahi2021overview,
title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection},
author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas},
journal={Working Notes of CLEF},
year={2021}
}
Problem Definition: Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in English and German.
Task 3: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. The training data will be released in batches and roughly about 1264 articles with the respective label in English language. Our definitions for the categories are as follows:
False - The main claim made in an article is untrue.
Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.
True - This rating indicates that the primary elements of the main claim are demonstrably true.
Other- An article that cannot be categorised as true, false, or partially false due to a lack of evidence about its claims. This category includes articles in dispute and unproven articles.
Cross-Lingual Task (German)
Along with the multi-class task for the English language, we have introduced a task for low-resourced language. We will provide the data for the test in the German language. The idea of the task is to use the English data and the concept of transfer to build a classification model for the German language.
Input Data
The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:
ID- Unique identifier of the news article
Title- Title of the news article
text- Text mentioned inside the news article
our rating - class of the news article as false, partially false, true, other
Output data format
public_id- Unique identifier of the news article
predicted_rating- predicted class
Sample File
public_id, predicted_rating
1, false
2, true
IMPORTANT!
We have used the data from 2010 to 2022, and the content of fake news is mixed up with several topics like elections, COVID-19 etc.
Baseline: For this task, we have created a baseline system. The baseline system can be found at https://zenodo.org/record/6362498
Related Work
Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.https://arxiv.org/pdf/2010.00502.pdf
G. K. Shahi and D. Nandini, “FakeCovid – a multilingual cross-domain fact check news dataset for covid-19,” in workshop Proceedings of the 14th International AAAI Conference on Web and Social Media, 2020. http://workshop-proceedings.icwsm.org/abstract?id=2020_14
Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104
Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. Working Notes of CLEF.
Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeno, A., Míguez, R., Shaar, S., ... & Mandl, T. (2021, March). The CLEF-2021 CheckThat! lab on detecting check-worthy claims, previously fact-checked claims, and fake news. In European Conference on Information Retrieval (pp. 639-649). Springer, Cham.
Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeño, A., Míguez, R., Shaar, S., ... & Kartal, Y. S. (2021, September). Overview of the CLEF–2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 264-291). Springer, Cham
Inertia-based connectivity matrices
No full textTwo connectivity matrices (one for schizophrenia patients and one for healthy controls)
Scripts needed to perform clusterin
CT-FAN-22 corpus: A Multilingual dataset for Fake News Detection
No full textData Access: The data in the research collection provided may only be used for research purposes. Portions of the data are copyrighted and have commercial value as data, so you must be careful to use them only for research purposes. Due to these restrictions, the collection is not open data. Please fill out the form and upload the Data Sharing Agreement at Google Form.
Citation
Please cite our work as
@article{shahi2021overview,
title={Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection},
author={Shahi, Gautam Kishore and Stru{\ss}, Julia Maria and Mandl, Thomas},
journal={Working Notes of CLEF},
year={2021}
}
Problem Definition: Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other (e.g., claims in dispute) and detect the topical domain of the article. This task will run in English and German.
Task 3: Multi-class fake news detection of news articles (English) Sub-task A would detect fake news designed as a four-class classification problem. The training data will be released in batches and roughly about 1264 articles with the respective label in English language. Given the text of a news article, determine whether the main claim made in the article is true, partially true, false, or other. Our definitions for the categories are as follows:
False - The main claim made in an article is untrue.
Partially False - The main claim of an article is a mixture of true and false information. The article contains partially true and partially false information but cannot be considered 100% true. It includes all articles in categories like partially false, partially true, mostly true, miscaptioned, misleading etc., as defined by different fact-checking services.
True - This rating indicates that the primary elements of the main claim are demonstrably true.
Other- An article that cannot be categorised as true, false, or partially false due to a lack of evidence about its claims. This category includes articles in dispute and unproven articles.
Cross-Lingual Task (German)
Along with the multi-class task for the English language, we have introduced a task for low resourced language. We will provide the data for test in the German language. The idea of the task is to use the English data and the concept of transfer to build a classification model for the German language.
Input Data
The data will be provided in the format of Id, title, text, rating, the domain; the description of the columns is as follows:
ID- Unique identifier of the news article
Title- Title of the news article
text- Text mentioned inside the news article
our rating - class of the news article as false, partially false, true, other
Output data format
public_id- Unique identifier of the news article
predicted_rating- predicted class
Sample File
public_id, predicted_rating
1, false
2, true
Additional data for Training
To train your model, the participant can use additional data with a similar format; some datasets are available over the web. We don't provide the background truth for those datasets. For testing, we will not use any articles from other datasets. Some of the possible sources:
Fakenews Classification Datasets
Fake News Detection Challenge KDD 2020
FakeNewsNet
IMPORTANT!
We have used the data from 2010 to 2022, and the content of fake news is mixed up with several topics like elections, COVID-19 etc.
Evaluation Metrics
This task is evaluated as a classification task. We will use the F1-macro measure for the ranking of teams. here is no limit to the number of submissions, we will evaluate the last submission from each team. Please mention your team name in each submission.
Baseline: For this task, we have created a baseline system. The baseline system can be found at https://zenodo.org/record/6362498
Submission Link: Codalab Page
Related Work
Shahi GK. AMUSED: An Annotation Framework of Multi-modal Social Media Data. arXiv preprint arXiv:2010.00502. 2020 Oct 1.https://arxiv.org/pdf/2010.00502.pdf
G. K. Shahi and D. Nandini, “FakeCovid – a multilingual cross-domain fact check news dataset for covid-19,” in workshop Proceedings of the 14th International AAAI Conference on Web and Social Media, 2020. http://workshop-proceedings.icwsm.org/abstract?id=2020_14
Shahi, G. K., Dirkson, A., & Majchrzak, T. A. (2021). An exploratory study of covid-19 misinformation on twitter. Online Social Networks and Media, 22, 100104. doi: 10.1016/j.osnem.2020.100104
Shahi, G. K., Struß, J. M., & Mandl, T. (2021). Overview of the CLEF-2021 CheckThat! lab task 3 on fake news detection. Working Notes of CLEF.
Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeno, A., Míguez, R., Shaar, S., ... & Mandl, T. (2021, March). The CLEF-2021 CheckThat! lab on detecting check-worthy claims, previously fact-checked claims, and fake news. In European Conference on Information Retrieval (pp. 639-649). Springer, Cham.
Nakov, P., Da San Martino, G., Elsayed, T., Barrón-Cedeño, A., Míguez, R., Shaar, S., ... & Kartal, Y. S. (2021, September). Overview of the CLEF–2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News. In International Conference of the Cross-Language Evaluation Forum for European Languages (pp. 264-291). Springer, Cham
Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs
No full text
Simulation experiment data for training Quantum Neural Networks (QNNs) using entangled datasets.
The experiments investigate the validity of the lower bounds for the expected risk after training QNNs given by the extensions to the Quantum No-Free-Lunch theorem presented in the related publication. The QNNs are trained with (i) samples of varying Schmidt rank, (ii) orthogonal samples of fixed Schmidt rank and (iii) linearly dependent samples of fixed Schmidt rank.
The dataset contains raw experiment data (directory "raw_data"), analyzed mean risks and errors (directory "plot_data") and the resulting plots (directory "plots").
Experiments:
The experiments train QNNs using various compositions of training samples on a simulator and extract the risk after training to compute average risks.
Experiment 1: Trains QNNs using entangled training samples of varying Schmidt rank. The average Schmidt rank and the number of training samples are controlled.
Raw data: average_rank_results.zip; Computed average risks: avg_rank_risks.npy; Computed average losses: avg_rank_losses.npy; Plotted average risks: avg_rank_experiments.pdf; Plotted average losses: avg_rank_losses.pdf.
Experiment 2: Trains QNNs using entangled orthogonal training samples. The number of training samples is controlled and the Schmidt rank is fixed such that d=r*t for the dimension d of the Hilbert space.
Raw data: orthogonal_results.zip; Computed average risks: orthogonal_exp_points.npy; Plotted average risks: orthogonal_experiments.pdf.
Experiment 3: Trains QNNs using entangled linearly dependent training samples. The number of training samples is controlled and the Schmidt rank is fixed such that d=r*t for the dimension d of the Hilbert space.
Raw data: not_linearly_independent_results.zip; Computed average risks: nlihx_exp_points.npy; Plotted average risks: nlihx_experiments.pdf
Additionally, this repository contains the reproduction data for Figure 1 (phases_in_orthogonal_training.zip). This file contains the training data, the target unitary and the resulting hypothesis unitary for orthogonal training samples of (i) high risk and (ii) low risk.
For the code to reproduce and analyze the experiments see the Code repository.
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Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Oxychila gracillima subsp. weyrauchi Mandl 1967
No full text<i>Oxychila gracillima weyrauchi</i> Mandl, 1967: 433 <p>HOLOTYPE male (TCOL139). Perú, Aramango, río Marañon, 3500 mts, IV.1960, Wosfkowski leg.</p> <p>Remarks: the original publication does not mention where the holotype is deposited. The abbreviation mts stands for meters.</p>Published as part of <i>Córdoba, Silvia Patricia, Sánchez, Francisco Rolando & Bezdjian, Laura Patricia, 2023, Types of Carabidae (Insecta: Coleoptera) deposited at the entomological collection of Instituto- Fundación Miguel Lillo, Tucumán, Argentina, pp. 151-189 in Zootaxa 5311 (2)</i> on page 154, DOI: 10.11646/zootaxa.5311.2.1, <a href="http://zenodo.org/record/8094048">http://zenodo.org/record/8094048</a>
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
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