Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulating Fake News Dissemination on Twitter with Multivariate Hawkes Processes

Disinformation and misinformation, spread over online social media, can be harmful to society. While there are a number of models that predict or explain the dynamic behavior of fake news dissemination over social media, the limited availability of datasets hinders the ability to fully test these models under a wide range of possible conditions. Simulation, however, offers a low-cost approach to evaluate models over many different scenarios. This paper proposes a novel approach for simulating fake news dissemination process on Twitter. Our approach combines a Multivariate Hawkes Point Processes with concepts from Agent-Based models, to generate realistic data that incorporates the core elements of Twitter including user networks, tweet type, user stance toward the news event, and time. The flexible and efficient simulation approach can capture a wide range of realistic behavior through a rich set of tuning parameters. We show how closely the simulated data can replicate real fake news events.
Exploring Multi-Time Context Vector and Randomness for Stock Movement Prediction

We propose a novel stock movement prediction model that learns Multi-Time Contexts and Randomness (MTC-R). We assume that the stock price movements are affected by (1) combination of time-based momentums and (2) tradings by noise traders which cause randomness in a stock market. MTC-R has three main procedures for modeling our hypothesis. First, the model learns time representations using time2vec and encodes the multi-time views using a GRU network. Second, MTC-R generates the multi-time contexts using a multi-head attention mechanism and inserts the randomness. Third, a loss function is designed to learn temporal differences between the results of the second and the current time-embedded vector. Our model improves the prediction results in terms of accuracy and the Matthews correlation coefficient on six benchmark datasets compared to baseline models. Furthermore, MCT-R shows the effectiveness of the prediction results using cumulative returns in portfolio trading simulations.
Predicting and Analyzing Privacy Settings and Categories for Posts on Social Media

While social media is prevalent in people’s daily life, privacy control of user-generated posts is becoming increasingly important. In this paper, we propose to enable automatic privacy control for social media posts through two tasks, predicting privacy settings and predicting privacy categories. The former is to recommend the proper settings of privacy levels, including family, close, casual, and outside, for a post. The latter is to predict the categories of privacy concerns for a post. We propose a multi-task learning-based approach, along with learning feature representation of each post, for such two tasks. Experiments conducted on a real dataset with tweet posts exhibit promising performance of our model, and thus encourage further investigation of privacy-related tasks for privacy control on social media. We also provide a series of extensive analysis with insights that reveal the hidden correlation between privacy settings/categories and post texts.
MUSCAT: Multilingual Rumor Detection in Social Media Conversations

The rapid spread of rumors on social media and their potential impact has motivated the development of automatic rumor detection solutions. However, the existing solutions are mostly limited to detecting rumors in English which neglects the bulk of social media content in other low-resource languages. This paper aims to address the research gaps by proposing Multilingual Source Co-Attention Transformer (MUSCAT), which builds on a multilingual pre-trained language model to perform multilingual rumor detection. Specifically, MUSCAT pivots the source claims in multilingual conversation threads with co-attention transformers to improve detection performance in multilingual settings. We additionally construct multilingual rumor datasets to support our experimental evaluations. Our experimental results show that MUSCAT outperforms state-of-the-art methods in monolingual, cross-lingual, and multilingual rumor detection settings. We have also conducted empirical analysis and outlined the challenges of performing rumor detection in multilingual and cross-lingual settings.
A 3D View of The Central Kiloparsec Regions of the Seyfert Galaxy NGC1358

We present a study of the stars and ionized gas in the central kpc of the Seyfert 2 galaxy NGC1358, using integral-field spectroscopy obtained with OASIS (CFHT). We have derived both stellar and gaseous kinematical maps, including higher order Gauss-Hermite stellar velocity moments. Emission line (Hα, Hβ, [O iii], [N ii], [S ii]) and emission line ratios ([O iii]/Hβ and [N ii]/Hα) maps have also been constructed. These data reveal an inner gaseous spiral structure (. 1 kpc) which clearly shows up as a perturbation in the ionised gas kinematic maps, but is not seen in the stellar velocity maps. The presence of an inner bar could explain this feature. Simple dynamical models are built to constrain the mass-to-light ratio of the galaxy and the properties of the presumed inner bar.
GUIDED COMPLEX WAVES: PART I. FIELDS AT AN INTERFACE

The variety of waves which can be supported by a plane homogeneous interface includes surface waves of the forward and backward type, and several kinds of complex wave, the latter being characterized by wave numbers which are complex even though the media involved are not necessarily lossy. The present study views all these waves as contributions due to poles in several alternative integral representations of a source-excited field, and places particular stress on the steepest-descent representation. The pole locations, field distributions and power-transport properties are explored in detail for all the wave types. Distinctions are made between proper (spectral, modal) and improper waves, and between lossy and lossless structures; complex waves along lossless structures are shown to appear always in degenerate pairs consisting of a forward and a backward wave, with interesting power-flow characteristics. The different wave types are grouped into the general category of guided complex waves which propagate without attenuation as inhomogeneous slow plane waves at some angle to the interface. Power-transport considerations via the steepest-descent representation show that these waves either carry power to compensate for losses in the system or account for a transfer of energy into the radiation field.
The Dynamics of Ukraine-Russian Conflict through the Lens of Demographically Diverse Twitter Data

Due to the disastrous global impact of the recent Russia-Ukraine conflict, people all over the world are getting concerned- expressing their opinions and feelings toward it on social media such as Twitter and Facebook. To comprehend people’s perceptions and reactions, in this study, we analyze around 80000 demographically diverse tweets representing Twitter users of 7 countries across four continents. We first perform sentiment analysis to reveal how sentiments are expressed across demographically diverse tweets. We notice a similar pattern in the conveyed opinions and sentiments across the groups, constituting a high number of negative tweets citing the disastrous consequence of the war and criticism of the aggression. In addition, we investigate the presence of highly offensive words and phrases in various demographic groups. Finally, we employ two topic modeling approaches, LDA and transformer-based BERTopic, to find the most prevalent and discussed topics across users of demographic groups. We witness the presence of a diverse set of topics in the tweets of various groups, with dominant themes conveying support for Ukraine, the destructive impact of the war, and the resolution of this conflict. Our analysis reveals the feelings, thoughts, and sentiments of demographically diverse Twitter users regarding this conflict and provides insights for understanding people’s perceptions across the globe.
Data Interpolation Methods by Inverse Distance Weighted Average Method between Multidisciplines

In this article, to handle data exchange between different grid systems efficiently and accuracy, the accuracy of inverse distance weighted average method is researched by different searching radius and exponent parameter. The result is compared with other two interpolation methods, radial basis function interpolation and local triangular projection method. The result shows that the search radius and exponential parameter of inverse distance weighted average interpolation method have not significant influence on interpolation result when radius is large.
Detecting Group Behavior for Anti-Money Laundering With Incomplete Network Information

Money laundering (ML) is a phenomenon that affects economies all around the world. Currently, ML is tackled by using automated transactions monitoring systems that sift through financial transactions and raise alerts in case some of them look illicit. Most of the existing monitoring systems are targeted to identify ML cases at the level of individual customers while ML is often a group action. In this paper, we present a case study of group ML behavior detection done under the assumption of incomplete network information available and the assumption of known ML patterns inaccessibility. The purpose of the research is to design an ML detection system that targets to detect group behavior and meets accuracy, actionability and scalability requirements. The proposed system has been developed within boundaries of one multinational financial institution operating over three separate jurisdictions. The system has been designed based on requirements provided by the investigative unit of the financial institution and developed on large scale financial data with real ML cases.
Transcriptional Regulation of Math1 by Aryl Hydrocarbon Receptor: Effect on Math1+ Progenitor Cells in Mouse Small Intestine

Abstract The physiological roles of aryl hydrocarbon receptor (AhR) in the small intestine have been revealed as immunomodulatory and barrier functions. However, its contributions to cell fate regulation are incompletely understood. The Notch-activated signaling cascade is a central component of intestinal cell fate determinations. The lateral inhibitory mechanism governed by Notch directs cell fates toward distinct cell lineages (i.e., absorptive and secretory cell lineages) through its downstream effector, mouse atonal homolog 1 (MATH1). An investigation employing cell lines and intestinal crypt cells revealed that AhR regulates Math1 expression in a xenobiotic response element (XRE)-dependent manner. The AhR-Math1 axis was further addressed using intestinal organoids, where AhR-Math1 and HES1-Math1 axes appeared to coexist within the underlying Math1 transcriptional machinery. When the HES1-Math1 axis was pharmacologically suppressed, β-naphthoflavone-mediated AhR activation increased the number of goblet and Math1+ progenitor cells in the organoids. The same pharmacological dissection of the AhR-Math1 axis was applied in vivo, demonstrating an enhanced number of Math1+ progenitor cells in the small intestine following AhR activation. We report here that AhR-Math1 is a direct transcriptional axis with effects on Math1+ progenitor cells in the small intestine, highlighting a novel molecular basis for fine-tuning Notch-mediated cell fate regulation.
Dipole angular entropy techniques for intron-exon segregation in DNA

We propose techniques for computing the angular entropies of DNA sequences, based on the orientations of the dipole moments of the nucleotide bases. The angles of the dipole moment vectors of the bases are used to compute the dipole angular entropy and the Fourier harmonics of the angles are used to compute the dipole angular spectral entropy for a given sequence. We also show that the coding (exons) and noncoding (introns) regions of the DNA can be segregated based on their dipole angular entropies and dipole angular spectral entropies. Segregation using these techniques is found to be computationally faster and more accurate than the previously reported methods. The proposed techniques can also be improvised to use the magnitude of the dipole moments of the bases in addition to the angles.
Research on Character Detection and Recognition Algorithm of Digital Instrument

At present, the traditional image processing and machine learning methods are mostly used for the recognition of character of digital instruments, the application scenarios of which is fixed and the restrictive conditions are many. In the complex and dynamic real environment, the recognition accuracy of character is rapidly reduced, which is difficult to meet the actual application requirements. Regarding the issue above, combining traditional image processing with deep learning, a method for detection and recognition of character based on deep convolutional neural networks is proposed. The method mainly includes three steps: screen area detection, character extraction, and recognition. First, the improved SSD deep convolutional neural network is applied to locate and segment the screen area of the instrument in the input image; Then, the visual saliency map of the screen area is calculated, and the characters are obtained by binarization the saliency map based on adaptive threshold; Finally, an end-to-end fully convolutional neural network is designed to recognize characters. The experimental results show that the proposed method can effectively improve the accuracy and robustness of character recognition. On the digital instrument dataset collected without restrictions, the overall recognition accuracy reaches 86.3 % and the detection speed is 10fps, which can meet the needs of practical application.
Age–Bilirubin–International Normalized Ratio (INR)–Creatinine (ABIC) Score, a Potential Prognostic Model for Long-Term Mortality of CAD Patients After PCI

Background Given that age, international normalized ratio (INR), total bilirubin, and creatinine are reported to be independent risk factors for predicting outcome in patients with coronary artery disease (CAD), it is possible that the age–bilirubin–INR–creatinine (ABIC) score might be a potential prognostic model for patients with CAD. Methods A total of 6046 CAD patients after percutaneous coronary intervention (PCI) from the retrospective cohort study (Identifier: ChiCTR-ORC-16010153) were evaluated finally. The primary outcome long-term mortality and secondary endpoints mainly major adverse cardiovascular and cerebrovascular events (MACCEs) were recorded. Multivariate Cox regression models were used to determine risk factors for mortality and MACCEs. Results The ABIC score was significantly higher in the death group than in the survival group. After adjusting for other CAD risk factors, the ABIC score was identified to be an independent risk factor for long-term mortality by multivariate Cox analysis. When in the high ABIC group, the incidence of all-cause mortality would increased 1.7 times (adjusted HR=1.729 (1.347–2.218), P<0.001), and 1.5 times for cardiac death (adjusted HR=1.482 (1.126–1.951), P=0.005). Conclusion The present study indicated that ABIC score≥7.985 predicts high long-term mortality and cardiac death risk for PCI patients. The ABIC score might be a potential prognostic model for patients with PCI.
Mobile Emotion Healthcare System Applying Sentiment analysis

Recent years have seen a boom in information technology, and the mobile mental healthcare system has also developed rapidly. However, it is necessary to understand users’ intention to use the system. Based on the Health Belief Model (HBM) and the Technology Acceptance Model (TAM), this study developed a usability evaluation model to assess users’ intention to use the system through experiments. It developed the Mobile Emotion Healthcare system based on machine learning and sentiment analysis techniques, which users can access with their smartphones to help them provide emotional care. The findings indicated that ‘health consciousness’, ‘perceived usefulness’, ‘perceived ease of use’ and ‘attitude’ all had a significant effect on users’ intention to use’ the Mobile Emotion Healthcare system.
Stability analysis and H∞ control design for a class of nonlinear time‐delay systems

This paper investigates the stability and H∞ control problem for a class of nonlinear time‐delay systems with a nonsingular Jacobian matrix, and provides a number of new results regarding stability analysis and control design. Firstly, an equivalent form is obtained for this class of systems by means of coordinate transformation and/or orthogonal decomposition of vector fields. Then, based on the equivalent form and free‐weighting matrix method, several sufficient conditions, in terms of nonlinear matrix inequalities, are derived for the stability analysis of the time‐delay systems by constructing suitable Lyapunov functionals. Finally, we use the equivalent form and the obtained stability results to investigate the H∞ control problem, and present a control design procedure for this class of time‐delay systems. A study of illustrative examples shows that the results obtained in this paper have less conservatism, and work very well in the stability analysis and control design of some nonlinear time‐delay systems.
Investigation of Bessel Functions in Analytical Modeling of Rotor Eddy Current Losses in PM Machine Rotor With Copper Shield

In high-speed surface-mounted permanent magnet (SPM) brushless machines, time- and space-harmonic excitations can induce significant eddy currents flowing in the rotor conductive parts. An effective measure to suppress the eddy currents is to employ a copper shield between the magnets and the retaining sleeve. In this article, a Fourier-based analytical model is proposed to evaluate the rotor eddy current losses. The model is set up in the cylindrical coordinate system and is able to compute the eddy current fields and losses in all rotor domains, that is, the sleeve, shield, magnets, and shaft. Although it is generally believed that both ordinary Bessel functions (OBFs) and modified Bessel functions (MBFs) are workable for solving the magnetic vector potential, it is argued in this article that only the MBF-based models are numerically stable when the high-conductivity domain (e.g., the copper shield) exists, while the OBF-based models may result in irrational results. Both the OBF- and MBF-based analytical models for a prototype motor are established and comparatively studied, with verification of the finite-element (FE) method. Moreover, the cause of losing analysis accuracy in the OBF-based models is revealed.
Subsurface morphology changes due to deuterium bombardment of tungsten

Recrystallized polycrystalline tungsten was exposed to a deuterium plasma beam with high flux (1022 D m−2 s−1) and low energy (38 eV D−1) to fluences up to 1027 D m−2. The sample temperature was varied between 320 and 800 K. The three-dimensional morphology of blister-like structures and the grain orientation were investigated by scanning electron microscopy combined with focused ion beam cross-sectioning and electron backscattering diffraction. Cracks with distorted areas (<650 K) and large cavities (100–1000 μm3) at grain boundaries (>480 K) were observed beneath the surface. The surface blister-like structures and the defects underneath are correlated along crystallographic orientation of the W grains in accordance to the low-indexed slip system {110}⟨111⟩. The defects are mobile and accumulate under deuterium loading. Samples exposed near room temperature do not form such large cavities by subsequent heating up to 1300 K. Deuterium bombardment above 700 K does not lead to blister-like structures.
Effect of heterogeneous microvasculature distribution on drug delivery to solid tumour

Most of the computational models of drug transport in vascular tumours assume a uniform distribution of blood vessels through which anti-cancer drugs are delivered. However, it is well known that solid tumours are characterized by dilated microvasculature with non-uniform diameters and irregular branching patterns. In this study, the effect of heterogeneous vasculature on drug transport and uptake is investigated by means of mathematical modelling of the key physical and biochemical processes in drug delivery. An anatomically realistic tumour model accounting for heterogeneous distribution of blood vessels is reconstructed based on magnetic resonance images of a liver tumour. Numerical simulations are performed for different drug delivery modes, including direct continuous infusion and thermosensitive liposome-mediated delivery, and the anti-cancer effectiveness is evaluated through changes in tumour cell density based on predicted intracellular concentrations. Comparisons are made between regions of different vascular density, and between the two drug delivery modes. Our numerical results show that both extra- and intra-cellular concentrations in the liver tumour are non-uniform owing to the heterogeneous distribution of tumour vasculature. Drugs accumulate faster in well-vascularized regions, where they are also cleared out more quickly, resulting in less effective tumour cell killing in these regions. Compared with direct continuous infusion, the influence of heterogeneous vasculature on anti-cancer effectiveness is more pronounced for thermosensitive liposome-mediated delivery.
Filling the gap: Noteworthy herpetological discoveries in North West Province, South Africa

The North West Province, South Africa, is centrally situated in southern Africa and is characterised by savannah with a mesic, temperate climate in the east and a hot, arid climate in the west. While the eastern region is fairly well-documented for herpetofauna, the arid central and western regions are poorly surveyed. Given that the Province has been targeted by the national government for development of infrastructure, the overall deficiency of biodiversity data could result in impact assessments that are not well-informed. We, therefore, carried out herpetofaunal surveys over two years (2019–2020) in the North West Province to improve knowledge on the distributions of reptiles and amphibians. Our surveys added a total of 578 new records to an earlier baseline of 1340 records. In addition, over 300 records were added to a citizen-science platform in connection with our surveys. As compared to the previous 100 years, our surveys increased the herpetofaunal dataset by 68% in just two years, increased geographic coverage by 20% and brought the total number of species with accurate records for the Province to 102 reptiles and 23 amphibians. We also recorded range extensions for five reptile species and confirmed the presence of Dendroaspis polylepis (Black Mamba) in the west where it had been last recorded in 1996. Our surveys resulted in a significant increase in biodiversity data for the Province and provided a better foundation for spatial planning that accounts for biodiversity and the maintenance of ecological function.
An experimental investigation for continuous contact recording technology

Key aspects for continuous contact recording technology were investigated experimentally. A slider with 0.7 mN contact force was used. Slider jump height and head positioning error were negligible with a low stiction media. Wear of head protective layer was predicted to be several nm through the drive lifetime. Mechanical spacing estimated from PW50 was 8 nm. These results proved feasibility of continuous contact recording scheme.