Multimodal Fake News Detection with Textual, Visual and Semantic Information

[EN] Recent years have seen a rapid growth in the number of fake news that are posted online. Fake news detection is very challenging since they are usually created to contain a mixture of false and real information and images that have been manipulated that confuses the readers. In this paper, we propose a multimodal system with the aim to di erentiate between fake and real posts. Our system is based on a neural network and combines textual, visual and semantic information. The textual information is extracted from the content of the post, the visual one from the image that is associated with the post and the semantic refers to the similarity between the image and the text of the post. We conduct our experiments on three standard real world collections and we show the importance of those features on detecting fake news.Anastasia Giachanou is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441). Guobiao Zhang is funded by China Scholarship Council (CSC) from the Ministry of Education of P.R. China. The work of Paolo Rosso is partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31)Giachanou, A.; Zhang, G.; Rosso, P. (2020). Multimodal Fake News Detection with Textual, Visual and Semantic Information. Springer. 30-38. https://doi.org/10.1007/978-3-030-58323-1_3S3038Boididou, C., et al.: Verifying multimedia use at MediaEval 2015. In: MediaEval 2015 Workshop, pp. 235–237 (2015)Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: WWW 2011, pp. 675–684 (2011)Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR 2017, pp. 1251–1258 (2017)Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. 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Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.Comment: The short version of this paper appears as oral paper in ECCV 201

Efficient Analysis of High Dimensional Data in Tensor Formats

In this article we introduce new methods for the analysis of high dimensional data in tensor formats, where the underling data come from the stochastic elliptic boundary value problem. After discretisation of the deterministic operator as well as the presented random fields via KLE and PCE, the obtained high dimensional operator can be approximated via sums of elementary tensors. This tensors representation can be effectively used for computing different values of interest, such as maximum norm, level sets and cumulative distribution function. The basic concept of the data analysis in high dimensions is discussed on tensors represented in the canonical format, however the approach can be easily used in other tensor formats. As an intermediate step we describe efficient iterative algorithms for computing the characteristic and sign functions as well as pointwise inverse in the canonical tensor format. Since during majority of algebraic operations as well as during iteration steps the representation rank grows up, we use lower-rank approximation and inexact recursive iteration schemes

Measuring Impact of Air and Agricultural Soil Pollution on Social Development in Saudi Arabia

This research aimed to measure the impact of air and agricultural soil pollution on social development in Saudi Arabia from the period 1995–2019 by using social development indicators, concentrating on the percentages of expenditure on education and health, and the Human Development Index. In addition, this study uses multiple regressions in estimating the model to study the impact of air pollution and agricultural soil on social development. Results of the study showed that a 10% change in the number of chemical fertilizers and pesticides used in Saudi agriculture leads to a change in the total number of inpatients by 0.7% and 0.5%, respectively. It was also found that an increased percentage of health expenditure to total government spending by 10% leads to a decrease in the total number of patients in the hospital by 1.8%. An increase in air pollution, expressed as a 10% increase in CO2 emissions, increases the total number of hospitalized patients by 11.1%.  The increasing total number of patients by 10% leads to a decrease in the total productivity of the worker, as an indicator of 1.8%. Furthermore, a change of 10% in the ratio of education expenditure to total government expenditure leads to a change in the same direction of the Human Development Index by 9.6%. In light of these results, it can be recommended that the country need to reduce air pollution by expanding the use of natural gas in the industrial and transportation sectors, in addition to reducing the use of nitrogenous fertilizers and pesticides in Saudi agriculture through the expansion of clean farming and good agricultural practices

On quantifying uncertainties for the linearized BGK kinetic equation

We consider the linearized BGK equation and want to quantify uncertainties in the case of modelling errors. More specifically, we want to quantify the error produced if the pre-determined equilibrium function is chosen inaccurately. In this paper we consider perturbations in the velocity and in the temperature of the equilibrium function and consider how much the error is amplified in the solution

The Impact of Histological Subtype on the Incidence, Timing, and Patterns of Recurrence in Patients with Renal Cell Carcinoma After Surgery-Results from RECUR Consortium

Background: Current follow-up strategies for patients with renal cell carcinoma (RCC) after curative surgery rely mainly on risk models and the treatment delivered, regardless of the histological subtype. Objective: To determine the impact of RCC histological subtype on recurrence and to examine the incidence, pattern, and timing of recurrences to improve follow-up recommendations. Design, setting, and participants: This study included consecutive patients treated surgically with curative intention (ie, radical and partial nephrectomy) for non-metastatic RCC (cT1-4, M0) between January 2006 and December 2011 across 15 centres from 10 countries, as part of the euRopEan association of urology renal cell carcinoma guidelines panel Collaborative multicenter consortium for the studies of follow-Up and recurrence patterns in Radically treated renal cell carcinoma patients (RECUR) database project. Outcome measurements and statistical analysis: The impact of histological subtype (ie, clear cell RCC [ccRCC], papillary RCC [pRCC], and chromophobe RCC [chRCC]) on recurrence-free survival (RFS) was assessed via univariate and multivariate analyses, adjusting for potential interactions with important variables (stage, grade, risk score, etc.) Patterns of recurrence for all histological subtypes were compared according to recurrence site and risk criteria. Results and limitations: Of the 3331 patients, 62.2% underwent radical nephrectomy and 37.8% partial nephrectomy. A total of 2565 patients (77.0%) had ccRCC, 535 (16.1%) had pRCC, and 231 (6.9%) had chRCC. The median postoperative follow-up period was 61.7 (interquartile range: 47-83) mo. Patients with ccRCC had significantly poorer 5-yr RFS than patients with pRCC and chRCC (78% vs 86% vs 91%, p = 0.001). The most common sites of recurrence for ccRCC were the lung and bone. Intermediate-/high-risk pRCC patients had an increased rate of lymphatic recurrence, both mediastinal and retroperitoneal, while recurrence in chRCC was rare (8.2%), associated with higher stage and positive margins, and predominantly in the liver and bone. Limitations include the retrospective nature of the study. Conclusions: The main histological subtypes of RCC exhibit a distinct pattern and dynamics of recurrence. Results suggest that intermediate- to high-risk pRCC may benefit from cross-sectional abdominal imaging every 6 mo until 2 yr after surgery, while routine imaging might be abandoned for chRCC except for abdominal computed tomography in patients with advanced tumour stage or positive margins. Patient summary: In this analysis of a large database from 15 countries around Europe, we found that the main histological subtypes of renal cell carcinoma have a distinct pattern and dynamics of recurrence. Patients should be followed differently according to subtype and risk score. (C) 2020 Published by Elsevier B.V. on behalf of European Association of Urology.Peer reviewe

Am J Hum Genet

Escobar syndrome is a form of arthrogryposis multiplex congenita and features joint contractures, pterygia, and respiratory distress. Similar findings occur in newborns exposed to nicotinergic acetylcholine receptor (AChR) antibodies from myasthenic mothers. We performed linkage studies in families with Escobar syndrome and identified eight mutations within the γ-subunit gene (CHRNG) of the AChR. Our functional studies show that γ-subunit mutations prevent the correct localization of the fetal AChR in human embryonic kidney–cell membranes and that the expression pattern in prenatal mice corresponds to the human clinical phenotype. AChRs have five subunits. Two α, one β, and one δ subunit are always present. By switching γ to ϵ subunits in late fetal development, fetal AChRs are gradually replaced by adult AChRs. Fetal and adult AChRs are essential for neuromuscular signal transduction. In addition, the fetal AChRs seem to be the guide for the primary encounter of axon and muscle. Because of this important function in organogenesis, human mutations in the γ subunit were thought to be lethal, as they are in γ-knockout mice. In contrast, many mutations in other subunits have been found to be viable but cause postnatally persisting or beginning myasthenic syndromes. We conclude that Escobar syndrome is an inherited fetal myasthenic disease that also affects neuromuscular organogenesis. Because γ expression is restricted to early development, patients have no myasthenic symptoms later in life. This is the major difference from mutations in the other AChR subunits and the striking parallel to the symptoms found in neonates with arthrogryposis when maternal AChR auto-antibodies crossed the placenta and caused the transient inactivation of the AChR pathway