Miracle-dz/UniFAR-FTUs huggingface dataset:
Community-Based Fact-Checking on Twitter's Birdwatch Platform

Misinformation undermines the credibility of social media and poses significant threats to modern societies. As a countermeasure, Twitter has recently introduced"Birdwatch,"a community-driven approach to address misinformation on Twitter. On Birdwatch, users can identify tweets they believe are misleading, write notes that provide context to the tweet and rate the quality of other users' notes. In this work, we empirically analyze how users interact with this new feature. For this purpose, we collect {all} Birdwatch notes and ratings between the introduction of the feature in early 2021 and end of July 2021. We then map each Birdwatch note to the fact-checked tweet using Twitter's historical API. In addition, we use text mining methods to extract content characteristics from the text explanations in the Birdwatch notes (e.g., sentiment). Our empirical analysis yields the following main findings: (i) users more frequently file Birdwatch notes for misleading than not misleading tweets. These misleading tweets are primarily reported because of factual errors, lack of important context, or because they treat unverified claims as facts. (ii) Birdwatch notes are more helpful to other users if they link to trustworthy sources and if they embed a more positive sentiment. (iii) The social influence of the author of the source tweet is associated with differences in the level of user consensus. For influential users with many followers, Birdwatch notes yield a lower level of consensus among users and community-created fact checks are more likely to be seen as being incorrect and argumentative. Altogether, our findings can help social media platforms to formulate guidelines for users on how to write more helpful fact checks. At the same time, our analysis suggests that community-based fact-checking faces challenges regarding opinion speculation and polarization among the user base.
The Role of Context in Detecting Previously Fact-Checked Claims

Recent years have seen the proliferation of disinformation and fake news online. Traditional approaches to mitigate these issues is to use manual or automatic fact-checking. Recently, another approach has emerged: checking whether the input claim has previously been fact-checked, which can be done automatically, and thus fast, while also offering credibility and explainability, thanks to the human fact-checking and explanations in the associated fact-checking article. Here, we focus on claims made in a political debate and we study the impact of modeling the context of the claim: both on the source side, i.e., in the debate, as well as on the target side, i.e., in the fact-checking explanation document. We do this by modeling the local context, the global context, as well as by means of co-reference resolution, and multi-hop reasoning over the sentences of the document describing the fact-checked claim. The experimental results show that each of these represents a valuable information source, but that modeling the source-side context is most important, and can yield 10+ points of absolute improvement over a state-of-the-art model.
Antimicrobial Activity and Analyses of Six Geranium L. Species with Headspace SPME and Hydrodistillation

Abstract A solid phase microextraction (SPME) method with gas chromatography-mass spectrometry (GC-MS) was used for analyses of the volatile compounds of six Geranium species; G. asphodeloides, G. psilostemon, G. purpureum, G. pyrenaicum, G. robertianum and G. sanguineum. The results were compared with those obtained by hydrodistillation. According to the results of the study, the major compounds identified in the SPME extracts were sabinen (33.5%) (G. asphodeloides), caryophyllene (34.1%, 21.7%, 11.2%) (G. psilostemon, G. purpureum and G. robertianum), germacrene D (25.2%) (G. pyrenaicum), and alloaromadendrene (19.8%) (G. sanguineum) whereas hydrodistillation (HD) essential oils were rich in benzene acetaldehyde (30%, 25.7%) (G. asphodeloides, G. sanguineum), caryophyllene (34.3%, 11.3%) (G. psilostemon and G. robertianum), hexadecanoic acid (36.2%, 15.1%) (G. purpureum and G. pyrenaicum). The oils were screened for antimicrobial activity against 10 microorganisms and showed antibacterial and antifungal activities against Staphylococcus aureus, Bacillus cereus, Mycobacterium smegmatis, Candida albicans and Saccharomyces cerevisiae.
Bilingual Terminology Extraction from Non-Parallel E-Commerce Corpora

Bilingual terminologies are important resources for natural language processing (NLP) applications. The acquisition of bilingual terminology pairs is either human translation or automatic extraction from parallel data. We notice that comparable corpora could also be a good resource for extracting bilingual terminology pairs, especially for e-commerce domain. The parallel corpora are particularly scarce in e-commerce settings, but the non-parallel corpora in different languages from the same domain are easily available. In this paper, we propose a novel framework of extracting bilingual terminologies from non-parallel comparable corpus in e-commerce. Beneﬁting from cross-lingual pre-training in e-commerce, our framework can extract the corresponding target terminology by fully utilizing the deep semantic relationship be-tween source-side terminology and target-side sentence. Experimental results on various language pairs show that our approaches achieve signiﬁ-cantly better performance than various strong base-lines. 1
Scale Invariant Solutions for Overdetermined Linear Systems with Applications to Reinforcement Learning

Abstract Overdetermined linear systems are common in reinforcement learning, e.g., in Q and value function estimation with function approximation. The standard least-squares criterion, however, leads to a solution that is unduly influenced by rows with large norms. This is a serious issue, especially when the matrices in these systems are beyond user control. To address this, we propose a scaleinvariant criterion that we then use to develop two novel algorithms for value function estimation: Normalized Monte Carlo and Normalized TD(0). Separately, we also introduce a novel adaptive stepsize that may be useful beyond this work as well. We use simulations and theoretical guarantees to demonstrate the efficacy of our ideas.
Hyperbolic Neural Collaborative Recommender

Recently, deep learning techniques have yielded immense success on recommender systems. However, one weakness of most deep methods is that, users/items mutual semantic relationships, which are latent in the user-item interactions, are not distilled out explicitly. Moreover, most methods have been primarily focused on representation learning in euclidean geometry. Since recent studies have shown that the bipartite graph structure has the non-euclidean latent anatomy, euclidean embeddings may suffer from a certain degree of distortion. In this work, we present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic representation learning method that exploits mutual semantic relationships among users/items for collaborative filtering tasks. HNCR first introduces a neighbor construction strategy to build user and item semantic neighborhoods. Then HNCR develops a framework based on deep learning and hyperbolic geometry to integrate constructed neighborhoods into recommendation. To evaluate our method, we conduct experiments on the four datasets. Experimental results show the superiority of HNCR compared with its euclidean counterpart and state-of-the-art recommendation baselines. The results also indicate that hyperbolic representations can reflect meaningful data insights.
V680 Mon – a young mercury–manganese star in an eclipsing heartbeat system

Chemically peculiar stars in eclipsing binary systems are rare objects that allow the derivation of fundamental stellar parameters and important information on evolutionary status and the origin of the observed chemical peculiarities. Here we present an investigation of the known eclipsing binary system BD+09 1467 = V680 Mon. Using spectra from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and own observations, we identify the primary component of the system as a mercury-manganese (HgMn/CP3) star (spectral type kB9 hB8 HeB9 V HgMn). Furthermore, photometric time series data from the Transiting Exoplanet Survey Satellite (TESS) indicate that the system is a ’heartbeat star’, a rare class of eccentric binary stars with short-period orbits that exhibit a characteristic signature near the time of periastron in their light curves due to the tidal distortion of the components. Using all available photometric observations, we present an updated ephemeris and binary system parameters as derived from a modelling of the system with the ELISa code, which indicate that the secondary star has an effective temperature of Te f f = 8300+200 −200 (spectral type ∼A4). V680 Mon is only the fifth known eclipsing CP3 star, and the first one in a heartbeat binary. Furthermore, our results indicate that the star is located on the zero-age main sequence and a possible member of the open cluster NGC 2264. As such, it lends itself perfectly for detailed studies and may turn out to be a keystone in the understanding of the development of CP3 star peculiarities.
Born–Oppenheimer meets Wigner–Weyl in quantum gravity

Starting from a Born–Oppenheimer decomposition of the Wheeler-DeWitt equation for the quantum cosmology of the matter–gravity system, we have performed a Wigner–Weyl transformation and obtained equations involving a Wigner function for the scale factor and its conjugate momentum. This has allowed us to study in more detail than previously the approach to the classical limit of gravitation and the way time emerges in such a limit. To lowest order we reproduce the Friedmann equation and the previously obtained equation for the evolution of matter. We also obtain expressions for higher order corrections to the semi-classical limit.
Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation

Building an effective Machine Learning (ML) model for a data set is a difficult task involving various steps. One of the most important steps is to compare a substantial amount of generated ML models to find the optimal one for deployment. It is challenging to compare such models with a dynamic number of features. Comparison is more than only finding differences of ML model performance, as users are also interested in the relations between features and model performance such as feature importance for ML explanations. This paper proposes RadialNet Chart, a novel visualisation approach, to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In RadialNet Chart, ML models and features are represented by lines and arcs, respectively. These lines are generated effectively using a recursive function. The dependence of ML models with a dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections. ML model performance information is encoded with colour and line width in RadialNet Chart. Taken together with the structure of visualisation, feature importance can be directly discerned in RadialNet Chart for ML explanations. Compared with other commonly used visualisation approaches, RadialNet Chart can help to simplify the ML model comparison process with different benefits such as the following: more efficient in terms of helping users to focus their attention to find visual elements of interest and easier to compare ML performance to find optimal ML model and discern important features visually and directly instead of through complex algorithmic calculations for ML explanations.
Logical Contradictions in the One-Way ANOVA and Tukey-Kramer Multiple Comparisons Tests with More Than Two Groups of Observations

We show that the one-way ANOVA and Tukey–Kramer (TK) tests agree on any sample with two groups. This result is based on a simple identity connecting the Fisher–Snedecor and studentized probabilistic distributions and is proven without any additional assumptions; in particular, the standard ANOVA assumptions (independence, normality, and homoscedasticity (INAH)) are not needed. In contrast, it is known that for a sample with k>2 groups of observations, even under the INAH assumptions, with the same significance level α, the above two tests may give opposite results: (i) ANOVA rejects its null hypothesis H0A:μ1=…=μk, while the TK one, H0TK(i,j):μi=μj, is not rejected for any pair i,j∈{1,…,k}; (ii) the TK test rejects H0TK(i,j) for a pair (i,j) (with i≠j), while ANOVA does not reject H0A. We construct two large infinite pseudo-random families of samples of both types satisfying INAH: in case (i) for any k≥3 and in case (ii) for some larger k. Furthermore, case (ii) ANOVA, being restricted to the pair of groups (i,j), may reject equality μi=μj with the same α. This is an obvious contradiction, since μ1=…=μk implies μi=μj for all i,j∈{1,…,k}. Such contradictions appear already in the symmetric case for k=3, or in other words, for three groups of d,d, and c observations with sample means +1,−1, and 0, respectively. We outline conditions necessary and sufficient for this phenomenon. Similar contradictory examples are constructed for the multivariable linear regression (MLR). However, for these constructions, it seems difficult to verify the Gauss–Markov assumptions, which are standardly required for MLR. Mathematics Subject Classification: 62 Statistics.
Thermal Desorption of Astrophysically Relevant Ice Mixtures of Acetaldehyde and Acetonitrile from Olivine Dust

Millimeter and centimeter observations are discovering an increasing number of interstellar complex organic molecules (iCOMs) in a large variety of star-forming sites, from the earliest stages of star formation to protoplanetary disks and in comets. In this context it is pivotal to understand how the solid-phase interactions between iCOMs and grain surfaces influence the thermal desorption process and, therefore, the presence of molecular species in the gas phase. In the laboratory, it is possible to simulate the thermal desorption process, deriving important parameters such as the desorption temperatures and energies. We report new laboratory results on temperature-programmed desorption from olivine dust of astrophysical relevant ice mixtures of water, acetonitrile, and acetaldehyde. We found that in the presence of grains, only a fraction of acetaldehyde and acetonitrile desorb at about 100 K and 120 K, respectively, while 40% of the molecules are retained by fluffy grains of the order of 100 μm up to temperatures of 190–210 K. In contrast with the typical assumption that all molecules are desorbed in regions with temperatures higher than 100 K, this result implies that about 40% of the molecules can survive on the grains enabling the delivery of volatiles toward regions with temperatures as high as 200 K and shifting inwards the position of the snow lines in protoplanetary disks. These studies offer a necessary support to interpret observational data and may help our understanding of iCOM formation, providing an estimate of the fraction of molecules released at various temperatures.
A Critique of Differential Abundance Analysis, and Advocacy for an Alternative

It is largely taken for granted that differential abundance analysis is, by default, the best first step when analyzing genomic data. We argue that this is not necessarily the case. In this article, we identify key limitations that are intrinsic to differential abundance analysis: it is (a) dependent on unverifiable assumptions, (b) an unreliable construct, and (c) overly reductionist. We formulate an alternative framework called ratio-based biomarker analysis which does not suffer from the identified limitations. Moreover, ratio-based biomarkers are highly flexible. Beyond replacing DAA, they can also be used for many other bespoke analyses, including dimension reduction and multi-omics data integration.
Camera View Adjustment Prediction for Improving Image Composition

Image composition plays an important role in the quality of a photo. However, not every camera user possesses the knowledge and expertise required for capturing well-composed photos. While post-capture cropping can improve the composition sometimes, it does not work in many common scenarios in which the photographer needs to adjust the camera view to capture the best shot. To address this issue, we propose a deep learning-based approach that provides suggestions to the photographer on how to adjust the camera view before capturing. By optimizing the composition before a photo is captured, our system helps photographers to capture better photos. As there is no publicly-available dataset for this task, we create a view adjustment dataset by repurposing existing image cropping datasets. Furthermore, we propose a two-stage semi-supervised approach that utilizes both labeled and unlabeled images for training a view adjustment model. Experiment results show that the proposed semi-supervised approach outperforms the corresponding supervised alternatives, and our user study results show that the suggested view adjustment improves image composition 79% of the time.
Failed excitonic quantum phase transition in 
Ta2Ni(Se1−xSx)5

We study the electronic phase diagram of the excitonic insulator candidates Ta2Ni(Se1−xSx)5 [x=0, . ,1] using polarization resolved Raman spectroscopy. Critical excitonic fluctuations are observed, that diminish with x and ultimately shift to high energies, characteristic of a quantum phase transition. Nonetheless, a symmetry-breaking transition at finite temperatures is detected for all x, exposing a cooperating lattice instability that takes over for large x. Our study reveals a failed excitonic quantum phase transition, masked by a preemptive structural order.
Structure and Dynamics of Molecules at Water/Silica and Water/Carbon Dioxide Interfaces

The interface between silica and water is one of the most technologically relevant surfaces. An especially important aspect of this system is its inherent negative charges at most pH values, and the resulting electrokinetic phenomena that take place in the fluid region. We have constructed a realistic model for the charged silica/water interface where many of these standard models can be tested. The model allows for undissociated and dissociated silanol groups. We have also conducted ab initio MD simulations of a smaller system consisting of a hydrated silica slab. The comparison of the radial distribution functions from the ab initio MD simulations and those obtained from the empirical model are favorable. The hydrophobic and hydrophilic nature of silanol-poor and silanol-rich regions of the amorphous silica surface observed in our empirical model is reproduced in the ab initio MD simulations of the smaller slab. In the initial stages of our ab initio MD simulations, we observe various chemical processes that represent different hydroxylation mechanisms of the surface. To explain why dynamical properties of an aqueous electrolyte near a charged surface seem to be governed by a surface charge less than the actual one, the canonical Stern model supposes an interfacial layer of ions and immobile fluid. However, large ion mobilities within the Stern layer are needed to reconcile the Stern model with surface conduction measurements. Modeling the aqueous electrolyte/amorphous silica interface at typical charge densities, a prototypical double layer system, the flow
The Effects of Transesterification and Blending on the Fatty Acid Profiles of Vegetable Oils

Vegetable oils are basically of fatty acids and the amount present can affect the properties of the resulting biodiesel. The aim of this study is to investigate how the transesterification affects the fatty acid profile of the resulting biodiesel produced. Seven vegetable oil types were selected for the study. They were transesterified and blended with diesel and the fatty acid profiles determined by method of gas chromatography. The results obtained shows that fatty acid profile of vegetable oils changes slightly after transesterification and mostly constant with blending.
Chronic total occlusion percutaneous coronary intervention in everyday clinical practice – an expert opinion of the Association of Cardiovascular Interventions of the Polish Cardiac Society

Coronary chronic total occlusions (CTO) are increasingly encountered during invasive and non-invasive coronary angiography and remain the most challenging lesions for percutaneous revascularization. During recent years success rates and safety outcomes of CTO percutaneous coronary intervention (PCI) have substantially improved, particularly due to the introduction of new techniques and dedicated equipment as well as specialized training programs of CTO operators. Significantly, the steady advances in CTO PCI techniques have coincided with the new data from randomized clinical trials supporting the role of percutaneous recanalization of CTO in relieving angina and improving the quality of life. The current expert consensus document outlines the rationale, clinical outcomes as well as technical, safety and reimbursement issues of CTO PCI. In addition, the requirements for achieving and maintaining competency in CTO PCI among interventional cardiologists are discussed. Finally, we present the modified hybrid algorithm (the so-called Polish hybrid algorithm) providing some unique refinements to the contemporary CTO PCI strategies. Continuous efforts (including active engagement with the payer) are urgently needed to increase guideline-recommended referrals to CTO PCI, and thus improve the quality of life of CTO patients in Poland.
Optimized culture methods for isolating small extracellular vesicles derived from human induced pluripotent stem cells

Abstract Extracellular vesicles that are derived from stem cells play an important role in the treatment of disease. To obtain high‐quality small extracellular vesicles (sEVs), we optimized the culture conditions of human induced pluripotent stem cells (hiPSCs), the supernatant collection time, and sEVs extraction methods. Firstly, hiPSCs were cultured in extracellular vesicles‐production medium (EVs‐PM) containing different concentrations (0%, 0.25%, 0.5%, 2%, 5%, and 20%) of extracellular vesicle‐depleted knockout serum replacement (ED‐KSR), and the culture supernatants were collected continuously for 5 days. Then, the sEVs were isolated, followed by an evaluation of their characteristics. The survival rates of the hiPSCs lines that were cultured in EVs‐PM containing 0.5% to 20% ED‐KSR were not significantly different (P > 0.05). The survival rates of the hiPSCs in 0.5% ED‐KSR after the culture supernatants were continuously collected for day 1, day 3, and day 5 were not statistically significant (P > 0.05). After 5 days of continuous collection of the supernatant, the hiPSCs expressed some pluripotent markers, while SSEA4 and TRA‐1‐60 expression changed gradually. The sEVs that were extracted by the two methods were all 50–200 nm, double‐layered and oval or round cellular vesicles and expressed the marker proteins CD63, TSG101, and HSP70. The characteristics of sEVs extracted on day 1, day 3, and day 5 were almost identical on morphology, size and the relative quantity of annexin V‐positive subpopulations. The PKH67 staining showed that the sEVs could be endocytosed by HepG2 cells and aggregated in the cytoplasm. The proliferation experiments showed that the sEVs can promote cell proliferation. In Conclusion, the 0.5% ED‐KSR is the optimal concentration, and that the hiPSCs culture supernatant can be continuously collected for 5 days while maintaining high cell viability and some pluripotent characteristics. Both of the methods extraction can be used to obtain biologically active sEVs.