FINFLUENCERS: OPINION MAKERS OR OPINION FOLLOWERS?

This paper explores the concept of Finfluencers: financial social network actors with high potential social influence. Our research aims to clarify whether Finfluencers drive or are influenced by the broader social network sentiment, thereby establishing their role as either opinion makers or opinion followers. Using a dataset of 71 million tweets focusing on stocks and cryptocurrencies, we grouped actors by their social networking potential (SNP). Next, we derived sentiment time series using state-ofthe- art sentiment models and applied the technique of Granger causality. Our findings suggest that the sentiment of Finfluencer actors on Twitter has short-term predictive power for the sentiment of the larger group of actors. We found stronger support for cryptocurrencies in comparison to stocks. From the perspective of financial market regulation, this study emphasizes the relevance of understanding sentiment on social networks and high social influence actors to anticipate scams and fraud

Detection of Stock Manipulation Influencer Content using Supervised Learning

In recent years, social media influencers have emerged as key players in stock manipulation schemes. Despite their growing impact, methods to detect such activities remain scarcely explored. In this study, we examine the social media content of stock manipulation influencers (SMIs) implicated in a $100 million fraud case by the U.S. Securities and Exchange Commission (SEC) in 2022. Leveraging natural language processing (NLP) techniques, we first investigate the linguistic characteristics present in the social media content published by SMIs. Next, we develop and evaluate supervised learning models to detect manipulative content. Our results have significant implications for investors, regulators, and the broader financial community. They reveal the unique linguistic characteristics of SMI content and demonstrate the potential of machine-learning and deep-learning-based techniques in advancing fraud detection systems

On the Involvement of Bots in Promote-Hit-and-Run Scams – The Case of Rug Pulls

[EN] Many social media frauds related to finance can be summarized under what we consider promote-hit-and-run scams. Examples include rug pull scams also known as exit scams, pump-and-dump schemes or bogus crypto currency trading platforms. For scams of this kind to work they must be publicly advertised as lucrative investment opportunities disguising the fraudulent motivation behind them. Social media are key in this promotion. Here, fraudsters find platforms to persuade others investing into what later turns out to be a scam. Via social network analysis of Twitter screen names and their first-level contacts, our work investigates rug pulls. It is aimed at profiling social media communication around them with a special focus on the deployment of bots. Repeatedly bots have been identified in social media campaigns (Orabi et al., 2020). Bot deployment in the context of rug pulls, however, has not been studied yet. Our analysis of social data of 27 rug pulls reveals massive bot activity coordinated within and between rug pulls mainly targeting established finance news outlets, e.g., Bloomberg, Reuters. Among the conclusions of our work is that bot deployment may prove an early indicator for rug pulls and other promote-hit-and-run scams.Federal Ministry of Education and Research of Germany (BMBF)Janetzko, D.; Krauß, J.; Haase, F.; Rath, O. (2023). On the Involvement of Bots in Promote-Hit-and-Run Scams – The Case of Rug Pulls. Editorial Universitat Politècnica de València. 187-194. https://doi.org/10.4995/CARMA2023.2023.1642818719

PERTURBATIONS IN THE SPECTRA OF HIGH RYDBERG STATES: CHANNEL INTERACTIONS, STARK AND ZEEMAN EFFECTS

Author Institution: Laboratorium fur Physikalische Chemie, ETH-Zurich, 8093 Zurich, SwitzerlandRydberg states of principal quantum number $n\gg50$ have been prepared by irradiation of an atomic beam of xenon with vacuum ultraviolet (VUV) radiation. Narrowband submillimeter-wave (THz) radiation was then used to record spectra of transitions from these Rydberg states to higher or lower-lying Rydberg states. The transitions were detected by selective field ionization and recording either the electrons or the ions, the latter offering the advantage of mass selection. Rydberg states of high principal quantum number are extremely sensitive to their environment, which can be exploited to characterize the experimental conditions under which the spectroscopic measurements are carried out nderline{\textbf{21}}, 385, (2002).}. The high resolution achieved in the experiments (better than 100~kHz) enabled the study of the spectral lineshapes and line positions in dependence of weak electric (down to below $100\mu$V/cm) and magnetic fields (down to a few $\mu$T), and of the density of Rydberg atoms and ions generated in the experimental volume. The experiments rely on the use of a pulsed, broadly tunable, laser-based source of THz radiation nderline{\textbf{93}}, 131105, (2008).} for survey scans, and of a phase- and frequency-stabilized submillimeter-wave backward-wave oscillator-based radiation source nderline{\textbf{39}}, 831, (2006).} for precision measurements. To illustrate the use of these sources, we present the results of the spectroscopic investigations of the hyperfine structure of $^{129}$Xe Rydberg states in spectral regions where s-d interactions are expected to play a role

VIBRATIONAL SPECTRA OF CHLOROFORM, FREON-11 AND SELECTED ISOTOPOMERS IN THE TERAHERTZ REGION

Author Institution: Laboratorium fur Physikalische Chemie, ETH-Zurich, 8093 Zurich, SwitzerlandThe fundamental bands of the CCl$_3$ asymmetric deformation modes of selected isotopomers of chloroform (CHCl$_3$) and freon-11 (CFCl$_3$) have been measured between 7 and 8~THz in a static cell at ambient temperature using a laser-based source of tunable radiation in the terahertz region (0.1-10~THz) of the electromagnetic spectrum. Simulation of the rotational contour of the $\nu_6$ fundamental transition of $^{12}$CH$^{35}$Cl$_3$ confirmed previously suggested values for $C_6$ and $C_6\zeta_6$. nderline{\textbf{170}}, 215, (1995).} The fundamental frequencies were derived with a precision of 2~GHz for all compounds except CF$^{35}$Cl$_3$, where the precision amounted to 3~GHz. The frequencies are in agreement with values calculated \textit{ab initio}. Extension of the experimental setup to enable measurement of THz spectra of molecules in supersonic jet expansions and partially resolve their rotational structure is in progress

Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

Our objective was to evaluate the diagnostic performance of a convolutional neural network (CNN) trained on multiple MR imaging features of the lumbar spine, to detect a variety of different degenerative changes of the lumbar spine. One hundred and forty-six consecutive patients underwent routine clinical MRI of the lumbar spine including T2-weighted imaging and were retrospectively analyzed using a CNN for detection and labeling of vertebrae, disc segments, as well as presence of disc herniation, disc bulging, spinal canal stenosis, nerve root compression, and spondylolisthesis. The assessment of a radiologist served as the diagnostic reference standard. We assessed the CNN’s diagnostic accuracy and consistency using confusion matrices and McNemar’s test. In our data, 77 disc herniations (thereof 46 further classified as extrusions), 133 disc bulgings, 35 spinal canal stenoses, 59 nerve root compressions, and 20 segments with spondylolisthesis were present in a total of 888 lumbar spine segments. The CNN yielded a perfect accuracy score for intervertebral disc detection and labeling (100%), and moderate to high diagnostic accuracy for the detection of disc herniations (87%; 95% CI: 0.84, 0.89), extrusions (86%; 95% CI: 0.84, 0.89), bulgings (76%; 95% CI: 0.73, 0.78), spinal canal stenoses (98%; 95% CI: 0.97, 0.99), nerve root compressions (91%; 95% CI: 0.89, 0.92), and spondylolisthesis (87.61%; 95% CI: 85.26, 89.21), respectively. Our data suggest that automatic diagnosis of multiple different degenerative changes of the lumbar spine is feasible using a single comprehensive CNN. The CNN provides high diagnostic accuracy for intervertebral disc labeling and detection of clinically relevant degenerative changes such as spinal canal stenosis and disc extrusion of the lumbar spine

TILDA: Towards Industrial LES/DNS in Aeronautics

This book offers detailed insights into new methods for high-fidelity CFD, and their industrially relevant applications in aeronautics. It reports on the H2020 TILDA project, funded by the European Union in 2015-2018. The respective chapters demonstrate the potential of high-order methods for enabling more accurate predictions of non-linear, unsteady flows, ensuring enhanced reliability in CFD predictions. The book highlights industrially relevant findings and representative test cases on the development of high-order methods for unsteady turbulence simulations on unstructured grids; on the development of the LES/DNS methodology by means of multilevel, adaptive, fractal and similar approaches for applications on unstructured grids; and on leveraging existent large-scale HPC networks to facilitate the industrial applications of LES/DNS in daily practice. Furthermore, the book discusses multidisciplinary applications of high-order methods in the area of aero-acoustics. All in all, it offers timely insights into the application and performance of high-order methods for CFD, and an extensive reference guide for researchers, graduate students, and industrial engineers whose work involves CFD and turbulence modeling

Proton Density Fat Fraction Spine MRI for Differentiation of Erosive Vertebral Endplate Degeneration and Infectious Spondylitis

Vertebral Modic type 1 (MT1) degeneration may mimic infectious disease on conventional spine magnetic resonance imaging (MRI), potentially leading to additional costly and invasive investigations. This study evaluated the diagnostic performance of the proton density fat fraction (PDFF) for distinguishing MT1 degenerative endplate changes from infectious spondylitis. A total of 31 and 22 patients with equivocal diagnosis of MT1 degeneration and infectious spondylitis, respectively, were retrospectively enrolled in this IRB-approved retrospective study and examined with a chemical-shift encoding (CSE)-based water-fat 3D six-echo modified Dixon sequence in addition to routine clinical spine MRI. Diagnostic reference standard was established according to histopathology or clinical and imaging follow-up. Intravertebral PDFF [%] and PDFFratio (i.e., vertebral endplate PDFF/normal vertebrae PDFF) were calculated voxel-wise within the single most prominent edematous bone marrow lesion per patient and examined for differences between MT1 degeneration and infectious spondylitis. Mean PDFF and PDFFratio of infectious spondylitis were significantly lower compared to MT1 degenerative changes (mean PDFF, 4.28 ± 3.12% vs. 35.29 ± 17.15% [p p p p < 0.001) and 98.1% (cut-off at 0.27) for PDFFratio. Our data suggest that quantitative evaluation of vertebral PDFF can provide a high diagnostic accuracy for differentiating erosive MT1 endplate changes from infectious spondylitis