On the linear increase of the flux tube thickness near the deconfinement transition

We study the flux tube thickness of a generic Lattice Gauge Theory near the deconfining phase transition. It is well known that the effective string model predicts a logarithmic increase of the flux tube thickness as a function of the interquark distance for any confining LGT at zero temperature. It is perhaps less known that this same model predicts a linear increase in the vicinity of the deconfinement transition. We present a precise derivation of this result and compare it with a set of high precision simulations in the case of the 3d gauge Ising model.Comment: 20 pages, 4 figures, minor changes. Accepted for publication in JHE

Band structure of semimagnetic Hg1-yMnyTe quantum wells

The band structure of semimagnetic Hg_1-yMn_yTe/Hg_1-xCd_xTe type-III quantum wells has been calculated using eight-band kp model in an envelope function approach. Details of the band structure calculations are given for the Mn free case (y=0). A mean field approach is used to take the influence of the sp-d exchange interaction on the band structure of QW's with low Mn concentrations into account. The calculated Landau level fan diagram and the density of states of a Hg_0.98Mn_0.02Te/Hg_0.3Cd_0.7Te QW are in good agreement with recent experimental transport observations. The model can be used to interpret the mutual influence of the two-dimensional confinement and the sp-d exchange interaction on the transport properties of Hg_1-yMn_yTe/Hg_1-xCd_xTe QW's.Comment: 12 pages, 4 figure

The Quantum Spin Hall Effect: Theory and Experiment

The search for topologically non-trivial states of matter has become an important goal for condensed matter physics. Recently, a new class of topological insulators has been proposed. These topological insulators have an insulating gap in the bulk, but have topologically protected edge states due to the time reversal symmetry. In two dimensions the helical edge states give rise to the quantum spin Hall (QSH) effect, in the absence of any external magnetic field. Here we review a recent theory which predicts that the QSH state can be realized in HgTe/CdTe semiconductor quantum wells. By varying the thickness of the quantum well, the band structure changes from a normal to an "inverted" type at a critical thickness $d_c$. We present an analytical solution of the helical edge states and explicitly demonstrate their topological stability. We also review the recent experimental observation of the QSH state in HgTe/(Hg,Cd)Te quantum wells. We review both the fabrication of the sample and the experimental setup. For thin quantum wells with well width $d_{QW}< 6.3$ nm, the insulating regime shows the conventional behavior of vanishingly small conductance at low temperature. However, for thicker quantum wells ($d_{QW}> 6.3$ nm), the nominally insulating regime shows a plateau of residual conductance close to $2e^2/h$. The residual conductance is independent of the sample width, indicating that it is caused by edge states. Furthermore, the residual conductance is destroyed by a small external magnetic field. The quantum phase transition at the critical thickness, $d_c= 6.3$ nm, is also independently determined from the occurrence of a magnetic field induced insulator to metal transition.Comment: Invited review article for special issue of JPSJ, 32 pages. For higher resolution figures see official online version when publishe

Investigation of BOLD using CARR-PURCELL T2 Weighting with SPIRAL Readout

It is demonstrated that a Carr-Purcell (CP) technique based on the fully adiabatic pulse sequence (CP-LASER) with SPIRAL readout can be used to generate zoomed images with relatively short acquisition window (at) for the investigation of the mechanisms of the BOLD effect. Based on the capability of the developed technique to refocus the dynamic dephasing, it is demonstrated that the BOLD effect is suppressed as the pulse interval cp of CP-LASER sequence decreased

Fluctuations of the baryonic flux-tube junction from effective string theory

In quenched QCD, where the dynamic creation of quark-antiquark pairs out of the vacuum is neglected, a confined baryonic system composed of three static quarks exhibits string-like behaviour at large interquark separation, with the formation of flux tubes characterized by the geometry of the so-called Y ansatz. We study the fluctuations of the junction of the three flux tubes, assuming the dynamics to be governed by an effective bosonic string model. We show that the asymptotic behaviour of the effective width of the junction grows logarithmically with the distance between the sources, with the coefficient depending on the number of joining strings, on the dimension of spacetime and on the string tension.Comment: 9 pages, 2 figures, RevTeX4 style, revision: style changed, references added, more explanations added, typos correcte

A pilot study assessing the clinical utility of deep learning-reconstructed 3D-echo-planar-imaging-based quantitative susceptibility mapping in multiple sclerosis

Background: Quantitative susceptibility mapping (QSM) has emerged as a promising paraclinical tool in multiple sclerosis (MS). This retrospective pilot study aims to evaluate whether a recently proposed deep learning-assisted, k-space-operating reconstruction, denoising and super-resolution technique (DLR) applied on 3D-echo-planar-imaging (3DEPI) protocols, has the potential to improve the quality and clinical utility of QSM in MS, at 3T. Secondarily, we assess whether applying DLR vs. a conventional reconstruction (CR) can improve the quality of QSM based on noise-susceptible, fast 3DEPI protocols.Methods: 3T MRI 3DEPI-data were acquired on seven MS patients and offline-reconstructed using CR and DLR. A sample size of 433 lesions was identified, based on FLAIR segmentation. Two experts, independently and method-blinded, rated lesion-wise the CR- and DLR-3DEPI-derived QSM, assessing the confidence in identifying paramagnetic rim lesions (PRLs), central vein sign (CVS), QSM hyper/isointense lesions and image quality. Gradient-recalled-echo (GRE), 2- and 1-average 3DEPI (acquisition time: 7:02, 3:44, and 1:56 min, respectively) from a healthy individual were offline-reconstructed using CR and DLR. Derived QSM maps were compared visually and quantitatively.Results: Deep learning reconstruction-3DEPI-based QSM was rated significantly higher for the confidence in identification of the MS-specific biomarkers (hyper/isointense lesions: P Conclusion: Our results constitute a first demonstration of the enhanced quality and clinical utility of the DLR-3DEPI-based QSM in MS

Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides

We have implemented the pypgatk package and the pgdb workflow to create proteogenomics databases based on ENSEMBL resources. The tools allow the generation of protein sequences from novel protein-coding transcripts by performing a three-frame translation of pseudogenes, lncRNAs and other non-canonical transcripts, such as those produced by alternative splicing events. It also includes exonic out-of-frame translation from otherwise canonical protein-coding mRNAs. Moreover, the tool enables the generation of variant protein sequences from multiple sources of genomic variants including COSMIC, cBioportal, gnomAD and mutations detected from sequencing of patient samples. pypgatk and pgdb provide multiple functionalities for database handling including optimized target/decoy generation by the algorithm DecoyPyrat. Finally, we have reanalyzed six public datasets in PRIDE by generating cell-type specific databases for 65 cell lines using the pypgatk and pgdb workflow, revealing a wealth of non-canonical or cryptic peptides amounting to >5% of the total number of peptides identified

Explanatory Interactive Machine Learning - Establishing an Action Design Research Process for Machine Learning Projects

The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cuttingedge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XILADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AIbased systems for organizations and research