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We show how nonlocal boundary conditions of Robin type can be encoded in the pointwise expression of the fractional operator. Notably, the fractional Laplacian of functions satisfying homogeneous nonlocal Neumann conditions can be expressed as a regional operator with a kernel having logarithmic behaviour at the boundary.
We show explicit formulas for the evaluation of (possibly higher-order) fractional Laplacians (-△)ˢ of some functions supported on ellipsoids. In particular, we derive the explicit expression of the torsion function and give examples of s-harmonic functions. As an application, we infer that the weak maximum principle fails in eccentric ellipsoids for s ∈ (1; √3 + 3/2) in any dimension n ≥ 2. We build a counterexample in terms of the torsion function times a polynomial of degree 2. Using point inversion transformations, it follows that a variety of bounded and unbounded domains do not satisfy positivity preserving properties either and we give some examples.
Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.
In “Fern von Aleppo”, the Syrian author Faisal Hamdo, who left his home in 2014 and sought refuge in Germany, tells of his very personal integration experiences. The book represents a kind of intercultural communication. In his book, Faisal Hamdo, who sees himself as a “mediator between the worlds”, tries to give the German reader answers to many questions regarding Syrian culture. From a text linguistic point of view, this book identifies the narrative development that seems to be tailored to the in-tercultural context. Accordingly, the present article raises the following questions: Does the structure of classic narration differ from the structure of narration in an intercultural context? Which intercultural information units are presented in the text? How are they embedded in the narrative text? Which constituents of the narrative structure are suitable for realizing intercultural communication? Which communicative functions do the constituents of the narrative structure fulfill in an intercultural context? The contribution sets itself the goal of analyzing the narrative structure to investigate how intercultural communication comes about through narration, how the intercultural information units are integrated into the constituents of the classic narrative structure so that they fulfill their communicative function, and to developa suitable analysis model.
This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.
Hepatitis Delta virus (HDV) is a satellite of Hepatitis B virus with a single-stranded circular RNA genome. HDV RNA genome synthesis is carried out in infected cells by cellular RNA polymerases with the assistance of the small hepatitis delta antigen (S-HDAg). Here we show that S-HDAg binds the bromodomain (BRD) adjacent to zinc finger domain 2B (BAZ2B) protein, a regulatory subunit of BAZ2B-associated remodeling factor (BRF) ISWI chromatin remodeling complexes. shRNA-mediated silencing of BAZ2B or its inactivation with the BAZ2B BRD inhibitor GSK2801 impairs HDV replication in HDV-infected human hepatocytes. S-HDAg contains a short linear interacting motif (SLiM) KacXXR, similar to the one recognized by BAZ2B BRD in histone H3. We found that the integrity of the S-HDAg SLiM sequence is required for S-HDAg interaction with BAZ2B BRD and for HDV RNA replication. Our results suggest that S-HDAg uses a histone mimicry strategy to co-activate the RNA polymerase II-dependent synthesis of HDV RNA and sustain HDV replication.
There has recently been a dramatic renewal of interest in hadron spectroscopy and charm physics. This renaissance has been driven in part by the discovery of a plethora of charmonium-like XYZ states at BESIII and B factories, and the observation of an intriguing proton-antiproton threshold enhancement and the possibly related X(1835) meson state at BESIII, as well as the threshold measurements of charm mesons and charm baryons.
We present a detailed survey of the important topics in tau-charm physics and hadron physics that can be further explored at BESIII during the remaining operation period of BEPCII. This survey will help in the optimization of the data-taking plan over the coming years, and provides physics motivation for the possible upgrade of BEPCII to higher luminosity.
Using a dedicated data sample taken in 2018 on the J/ψ peak, we perform a detailed study of the trigger efficiencies of the BESIII detector. The efficiencies are determined from three representative physics processes, namely Bhabha scattering, dimuon production and generic hadronic events with charged particles. The combined efficiency of all active triggers approaches 100% in most cases, with uncertainties small enough not to affect most physics analyses.
Ten hadronic final states of the ℎ𝑐 decays are investigated via the process 𝜓(3686)→𝜋0ℎ𝑐, using a data sample of (448.1±2.9)×106 𝜓(3686) events collected with the BESIII detector. The decay channel ℎ𝑐→𝐾+𝐾−𝜋+𝜋−𝜋0 is observed for the first time and has a measured significance of 6.0𝜎. The corresponding branching fraction is determined to be ℬ(ℎ𝑐→𝐾+𝐾−𝜋+𝜋−𝜋0)=(3.3±0.6±0.6)×10−3 (where the uncertainties are statistical and systematic, respectively). Evidence for the decays ℎ𝑐→𝜋+𝜋−𝜋0𝜂 and ℎ𝑐→𝐾0𝑆𝐾±𝜋∓𝜋+𝜋− is found with a significance of 3.6𝜎 and 3.8𝜎, respectively. The corresponding branching fractions (and upper limits) are obtained to be ℬ(ℎ𝑐→𝜋+𝜋−𝜋0𝜂)=(7.2±1.8±1.3)×10−3 (<1.8×10−2) and ℬ(ℎ𝑐→𝐾0𝑆𝐾±𝜋∓𝜋+𝜋−)=(2.8±0.9±0.5)×10−3 (<4.7×10−3). Upper limits on the branching fractions for the final states ℎ𝑐→𝐾+𝐾−𝜋0, 𝐾+𝐾−𝜂, 𝐾+𝐾−𝜋+𝜋−𝜂, 2(𝐾+𝐾−)𝜋0, 𝐾+𝐾−𝜋0𝜂, 𝐾0𝑆𝐾±𝜋∓, and 𝑝¯𝑝𝜋0𝜋0 are determined at a confidence level of 90%.
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7.5~fb−1 of e+e− collision data at center-of-mass energies ranging from 4.13 to 4.44~GeV. These data samples are primarily used for the study of excited charmonium and charmoniumlike states. By analyzing the di-muon process e+e−→(γISR/FSR)μ+μ−, we measure the center-of-mass energies of the data samples with a precision of 0.6 MeV. Through a run-by-run study, we find that the center-of-mass energies were stable throughout most of the data-taking period.