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The ongoing digitalization of educational resources and the use of the internet lead to a steady increase of potentially available learning media. However, many of the media which are used for educational purposes have not been designed specifically for teaching and learning. Usually, linguistic criteria of readability and comprehensibility as well as content-related criteria are used independently to assess and compare the quality of educational media. This also holds true for educational media used in economics. This article aims to improve the analysis of textual learning media used in economic education by drawing on threshold concepts. Threshold concepts are key terms in knowledge acquisition within a domain. From a linguistic perspective, however, threshold concepts are instances of specialized vocabularies, exhibiting particular linguistic features. In three kinds of (German) resources, namely in textbooks, in newspapers, and on Wikipedia, we investigate the distributive profiles of 63 threshold concepts identified in economics education (which have been collected from threshold concept research). We looked at the threshold concepts' frequency distribution, their compound distribution, and their network structure within the three kinds of resources. The two main findings of our analysis show that firstly, the three kinds of resources can indeed be distinguished in terms of their threshold concepts' profiles. Secondly, Wikipedia definitely shows stronger associative connections between economic threshold concepts than the other sources. We discuss the findings in relation to adequate media use for teaching and learning—not only in economic education.
Co-design of a trustworthy AI system in healthcare: deep learning based skin lesion classifier
(2021)
This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.
In this talk we presented a novel technique, based on Deep Learning, to determine the impact parameter of nuclear collisions at the CBM experiment. PointNet based Deep Learning models are trained on UrQMD followed by CBMRoot simulations of Au+Au collisions at 10 AGeV to reconstruct the impact parameter of collisions from raw experimental data such as hits of the particles in the detector planes, tracks reconstructed from the hits or their combinations. The PointNet models can perform fast, accurate, event-by-event impact parameter determination in heavy ion collision experiments. They are shown to outperform a simple model which maps the track multiplicity to the impact parameter. While conventional methods for centrality classification merely provide an expected impact parameter distribution for a given centrality class, the PointNet models predict the impact parameter from 2–14 fm on an event-by-event basis with a mean error of −0.33 to 0.22 fm.
The ALICE Collaboration reports the first fully-corrected measurements of the N-subjettiness observable for track-based jets in heavy-ion collisions. This study is performed using data recorded in pp and Pb-Pb collisions at centre-of-mass energies of s√ = 7 TeV and sNN−−−√ = 2.76 TeV, respectively. In particular the ratio of 2-subjettiness to 1-subjettiness, τ2/τ1, which is sensitive to the rate of two-pronged jet substructure, is presented. Energy loss of jets traversing the strongly interacting medium in heavy-ion collisions is expected to change the rate of two-pronged substructure relative to vacuum. The results are presented for jets with a resolution parameter of R = 0.4 and charged jet transverse momentum of 40 ≤ pT,jet ≤ 60 GeV/c, which constitute a larger jet resolution and lower jet transverse momentum interval than previous measurements in heavy-ion collisions. This has been achieved by utilising a semi-inclusive hadron-jet coincidence technique to suppress the larger jet combinatorial background in this kinematic region. No significant modification of the τ2/τ1 observable for track-based jets in Pb-Pb collisions is observed relative to vacuum PYTHIA6 and PYTHIA8 references at the same collision energy. The measurements of τ2/τ1, together with the splitting aperture angle ∆R, are also performed in pp collisions at s√ = 7 TeV for inclusive jets. These results are compared with PYTHIA calculations at s√ = 7 TeV, in order to validate the model as a vacuum reference for the Pb-Pb centre-of-mass energy. The PYTHIA references for τ2/τ1 are shifted to larger values compared to the measurement in pp collisions. This hints at a reduction in the rate of two-pronged jets in Pb-Pb collisions compared to pp collisions.
Production of pions, kaons, (anti-)protons and φ mesons in Xe–Xe collisions at √sNN = 5.44 TeV
(2021)
The first measurement of the production of pions, kaons, (anti-)protons and φ mesons at midrapidity in Xe–Xe collisions at √sNN = 5.44 TeV is presented. Transverse momentum (pT) spectra and pT-integrated yields are extracted in several centrality intervals bridging from p–Pb to mid-central Pb–Pb collisions in terms of final-state multiplicity. The study of Xe–Xe and Pb–Pb collisions allows systems at similar charged-particle multiplicities but with different initial geometrical eccentricities to be investigated. A detailed comparison of the spectral shapes in the two systems reveals an opposite behaviour for radial and elliptic flow. In particular, this study shows that the radial flow does not depend on the colliding system when compared at similar charged-particle multiplicity. In terms of hadron chemistry, the previously observed smooth evolution of particle ratios with multiplicity from small to large collision systems is also found to hold in Xe–Xe. In addition, our results confirm that two remarkable features of particle production at LHC energies are also valid in the collision of medium-sized nuclei: the lower proton-to-pion ratio with respect to the thermal model expectations and the increase of the φ-to-pion ratio with increasing final-state multiplicity.
The inclusive production of the J/ψ and ψ(2S) charmonium states is studied as a function of centrality in p-Pb collisions at a centre-of-mass energy per nucleon pair sNN−−−√ = 8.16 TeV at the LHC. The measurement is performed in the dimuon decay channel with the ALICE apparatus in the centre-of-mass rapidity intervals −4.46 < ycms < −2.96 (Pb-going direction) and 2.03 < ycms < 3.53 (p-going direction), down to zero transverse momentum (pT). The J/ψ and ψ(2S) production cross sections are evaluated as a function of the collision centrality, estimated through the energy deposited in the zero degree calorimeter located in the Pb-going direction. The pT-differential J/ψ production cross section is measured at backward and forward rapidity for several centrality classes, together with the corresponding average 〈pT〉 and ⟨p2T⟩ values. The nuclear effects affecting the production of both charmonium states are studied using the nuclear modification factor. In the p-going direction, a suppression of the production of both charmonium states is observed, which seems to increase from peripheral to central collisions. In the Pb-going direction, however, the centrality dependence is different for the two states: the nuclear modification factor of the J/ψ increases from below unity in peripheral collisions to above unity in central collisions, while for the ψ(2S) it stays below or consistent with unity for all centralities with no significant centrality dependence. The results are compared with measurements in p-Pb collisions at sNN−−−√ = 5.02 TeV and no significant dependence on the energy of the collision is observed. Finally, the results are compared with theoretical models implementing various nuclear matter effects.
Jet fragmentation transverse momentum distributions in pp and p-Pb collisions at √s, √sNN = 5.02 TeV
(2021)
Jet fragmentation transverse momentum (jT) distributions are measured in proton-proton (pp) and proton-lead (p-Pb) collisions at sNN−−−√ = 5.02 TeV with the ALICE experiment at the LHC. Jets are reconstructed with the ALICE tracking detectors and electromagnetic calorimeter using the anti-kT algorithm with resolution parameter R = 0.4 in the pseudorapidity range |η| < 0.25. The jT values are calculated for charged particles inside a fixed cone with a radius R = 0.4 around the reconstructed jet axis. The measured jT distributions are compared with a variety of parton-shower models. Herwig and PYTHIA 8 based models describe the data well for the higher jT region, while they underestimate the lower jT region. The jT distributions are further characterised by fitting them with a function composed of an inverse gamma function for higher jT values (called the “wide component”), related to the perturbative component of the fragmentation process, and with a Gaussian for lower jT values (called the “narrow component”), predominantly connected to the hadronisation process. The width of the Gaussian has only a weak dependence on jet transverse momentum, while that of the inverse gamma function increases with increasing jet transverse momentum. For the narrow component, the measured trends are successfully described by all models except for Herwig. For the wide component, Herwig and PYTHIA 8 based models slightly underestimate the data for the higher jet transverse momentum region. These measurements set constraints on models of jet fragmentation and hadronisation.
Themultiplicity dependence of the pseudorapidity density of charged particles in proton–proton (pp) collisions at centre-of-mass energies √s = 5.02, 7 and 13 TeV measured by ALICE is reported. The analysis relies on track segments measured in the midrapidity range (|η| < 1.5). Results are presented for inelastic events having at least one charged particle produced in the pseudorapidity interval |η| < 1. The multiplicity dependence of the pseudorapidity density of charged particles is measured with mid- and forward rapidity multiplicity estimators, the latter being less affected by autocorrelations.Adetailed comparison with predictions from the PYTHIA 8 and EPOS LHC event generators is also presented. The results can be used to constrain models for particle production as a function of multiplicity in pp collisions.
Measurements of elliptic (v2) and triangular (v3) flow coefficients of π±, K±, p+p¯¯¯, K0S, and Λ+Λ¯¯¯¯ obtained with the scalar product method in Xe-Xe collisions at sNN−−−√ = 5.44 TeV are presented. The results are obtained in the rapidity range |y| < 0.5 and reported as a function of transverse momentum, pT, for several collision centrality classes. The flow coefficients exhibit a particle mass dependence for pT < 3 GeV/c, while a grouping according to particle type (i.e., meson and baryon) is found at intermediate transverse momenta (3 < pT < 8 GeV/c). The magnitude of the baryon v2 is larger than that of mesons up to pT = 6 GeV/c. The centrality dependence of the shape evolution of the pT-differential v2 is studied for the various hadron species. The v2 coefficients of π±, K±, and p+p¯¯¯ are reproduced by MUSIC hydrodynamic calculations coupled to a hadronic cascade model (UrQMD) for pT < 1 GeV/c. A comparison with vn measurements in the corresponding centrality intervals in Pb-Pb collisions at sNN−−−√ = 5.02 TeV yields an enhanced v2 in central collisions and diminished value in semicentral collisions.
Two-particle angular correlations are measured in high-multiplicity proton-proton collisions at s√ = 13 TeV by the ALICE Collaboration. The yields of particle pairs at short-(∆η ∼ 0) and long-range (1.6 < |∆η| < 1.8) in pseudorapidity are extracted on the near-side (∆φ ∼ 0). They are reported as a function of transverse momentum (pT) in the range 1 < pT < 4 GeV/c. Furthermore, the event-scale dependence is studied for the first time by requiring the presence of high-pT leading particles or jets for varying pT thresholds. The results demonstrate that the long-range “ridge” yield, possibly related to the collective behavior of the system, is present in events with high-pT processes as well. The magnitudes of the short- and long-range yields are found to grow with the event scale. The results are compared to EPOS LHC and PYTHIA 8 calculations, with and without string-shoving interactions. It is found that while both models describe the qualitative trends in the data, calculations from EPOS LHC show a better quantitative agreement for the pT dependency, while overestimating the event-scale dependency.
The transverse momentum (pT) differential cross section of the charm-strange baryon Ξ0c is measured at midrapidity (|y| < 0.5) via its semileptonic decay into e+Ξ−νe in pp collisions at s√ = 5.02 TeV with the ALICE detector at the LHC. The ratio of the pT-differential Ξ0c-baryon and D0-meson production cross sections is also reported. The measurements are compared with simulations with different tunes of the PYTHIA 8 event generator, with predictions from a statistical hadronisation model (SHM) with a largely augmented set of charm-baryon states beyond the current lists of the Particle Data Group, and with models including hadronisation via quark coalescence. The pT-integrated cross section of prompt Ξ0c-baryon production at midrapidity is also reported, which is used to calculate the baryon-to-meson ratio Ξ0c/D0 = 0.20 ± 0.04 (stat.)+0.08−0.07 (syst.). These results provide an additional indication of a modification of the charm fragmentation from e+e− and e−p collisions to pp collisions.
A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior. Abstract representations ignore specific details and facilitate generalization. Here we consider the learning of abstract representations in a multi-modal setting with two or more input modalities. We treat the problem as a lossy compression problem and show that generic lossy compression of multimodal sensory input naturally extracts abstract representations that tend to strip away modalitiy specific details and preferentially retain information that is shared across the different modalities. Furthermore, we propose an architecture to learn abstract representations by identifying and retaining only the information that is shared across multiple modalities while discarding any modality specific information.
In the last two decades, our understanding of human gene regulation has improved tremendously. There are plentiful computational methods which focus on integrative data analysis of humans, and model organisms, like mouse and drosophila. However, these tools are not directly employable by researchers working on non-model organisms to answer fundamental biological, and evolutionary questions. We aimed to develop new tools, and adapt existing software for the analysis of transcriptomic and epigenomic data of one such non-model organism, Paramecium tetraurelia, an unicellular eukaryote. Paramecium contains two diploid (2n) germline micronuclei (MIC) and a polyploid (800n) somatic macronuclei (MAC). The transcriptomic and epigenomic regulatory landscape of the MAC genome, which has 80% protein-coding genes and short intergenic regions, is poorly understood.
We developed a generic automated eukaryotic short interfering RNA (siRNA) analysis tool, called RAPID. Our tool captures diverse siRNA characteristics from small RNA sequencing data and provides easily navigable visualisations. We also introduced a normalisation technique to facilitate comparison of multiple siRNA-based gene knockdown studies. Further, we developed a pipeline to characterise novel genome-wide endogenous short interfering RNAs (endo-siRNAs). In contrary to many organisms, we found that the endo-siRNAs are not acting in cis, to silence their parent mRNA. We also predicted phasing of siRNAs, which are regulated by the RNA interference (RNAi) pathway.
Further, using RAPID, we investigated the aberrations of endo-siRNAs, and their respective transcriptomic alterations caused by an RNAi pathway triggered by feeding small RNAs against a target gene. We find that the small RNA transcriptome is altered, even if a gene unrelated to RNAi pathway is targeted. This is important in the context of investigations of genetically modified organisms (GMOs). We suggest that future studies need to distinguish transcriptomic changes caused by RNAi inducing techniques and actual regulatory changes.
Subsequently, we adapted existing epigenomics analysis tools to conduct the first comprehensive epigenomic characterisation of nucleosome positioning and histone modifications of the Paramecium MAC. We identified well positioned nucleosomes shifted downstream of the transcription start site. GC content seems to dictate, in cis, the positioning of nucleosomes, histone marks (H3K4me3, H3K9ac, and H3K27me3), and Pol II in the AT-rich Paramecium genome. We employed a chromatin state segmentation approach, on nucleosomes and histone marks, which revealed genes with active, repressive, and bivalent chromatin states. Further, we constructed a regulatory association network of all the aforementioned data, using the sparse partial correlation network technique. Our analysis revealed subsets of genes, whose expression is positively associated with H3K27me3, different to the otherwise reported negative association with gene expression in many other organisms.
Further, we developed a Random Forests classifier to predict gene expression using genic (gene length, intron frequency, etc.) and epigenetic features. Our model has a test performance (PR-AUC) of 0.83. Upon evaluating different feature sets, we found that genic features are as predictive, of gene expression, as the epigenetic features. We used Shapley local feature explanation values, to suggest that high H3K4me3, high intron frequency, low gene length, high sRNA, and high GC content are the most important elements for determining gene expression status.
In this thesis, we developed novel tools, and employed several bioinformatics and machine learning methods to characterise the regulatory landscape of the Paramecium’s (epi)genome.
The study of (anti-)deuteron production in pp collisions has proven to be a powerful tool to investigate the formation mechanism of loosely bound states in high energy hadronic collisions. In this paper the production of (anti-)deuterons is studied as a function of the charged particle multiplicity in inelastic pp collisions at s√=13 TeV using the ALICE experiment. Thanks to the large number of accumulated minimum bias events, it has been possible to measure (anti-)deuteron production in pp collisions up to the same charged particle multiplicity (dNch/dη∼26) as measured in p-Pb collisions at similar centre-of-mass energies. Within the uncertainties, the deuteron yield in pp collisions resembles the one in p-Pb interactions, suggesting a common formation mechanism behind the production of light nuclei in hadronic interactions. In this context the measurements are compared with the expectations of coalescence and Statistical Hadronisation Models (SHM).
Point-based geometry representations have become widely used in numerous contexts, ranging from particle-based simulations, over stereo image matching, to depth sensing via light detection and ranging. Our application focus is on the reconstruction of curved line structures in noisy 3D point cloud data. Respective algorithms operating on such point clouds often rely on the notion of a local neighborhood. Regarding the latter, our approach employs multi-scale neighborhoods, for which weighted covariance measures of local points are determined. Curved line structures are reconstructed via vector field tracing, using a bidirectional piecewise streamline integration. We also introduce an automatic selection of optimal starting points via multi-scale geometric measures. The pipeline development and choice of parameters was driven by an extensive, automated initial analysis process on over a million prototype test cases. The behavior of our approach is controlled by several parameters — the majority being set automatically, leaving only three to be controlled by a user. In an extensive, automated final evaluation, we cover over one hundred thousand parameter sets, including 3D test geometries with varying curvature, sharp corners, intersections, data holes, and systematically applied varying types of noise. Further, we analyzed different choices for the point of reference in the co-variance computation; using a weighted mean performed best in most cases. In addition, we compared our method to current, publicly available line reconstruction frameworks. Up to thirty times faster execution times were achieved in some cases, at comparable error measures. Finally, we also demonstrate an exemplary application on four real-world 3D light detection and ranging datasets, extracting power line cables.
Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimisation steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive.
When performing transfer learning in Computer Vision, normally a pretrained model (source model) that is trained on a specific task and a large dataset like ImageNet is used. The learned representation of that source model is then used to perform a transfer to a target task. Performing transfer learning in this way had a great impact on Computer Vision, because it worked seamlessly, especially on tasks that are related to each other. Current research topics have investigated the relationship between different tasks and their impact on transfer learning by developing similarity methods. These similarity methods have in common, to do transfer learning without actually doing transfer learning in the first place but rather by predicting transfer learning rankings so that the best possible source model can be selected from a range of different source models. However, these methods have focused only on singlesource transfers and have not paid attention to multi-source transfers. Multi-source transfers promise even better results than single-source transfers as they combine information from multiple source tasks, all of which are useful to the target task. We fill this gap and propose a many-to-one task similarity method called MOTS that predicts both, single-source transfers and multi-source transfers to a specific target task. We do that by using linear regression and the source representations of the source models to predict the target representation. We show that we achieve at least results on par with related state-of-the-art methods when only focusing on singlesource transfers using the Pascal VOC and Taskonomy benchmark. We show that we even outperform all of them when using single and multi-source transfers together (0.9 vs. 0.8) on the Taskonomy benchmark. We additionally investigate the performance of MOTS in conjunction with a multi-task learning architecture. The task-decoder heads of a multi-task learning architecture are used in different variations to do multi-source transfers since it promises efficiency over multiple singletask architectures and incurs less computational cost. Results show that our proposed method accurately predicts transfer learning rankings on the NYUD dataset and even shows the best transfer learning results always being achieved when using more than one source task. Additionally, it is further examined that even just using one task-decoder head from the multi-task learning architecture promises better transfer learning results, than using a single-task architecture for the same task, which is due to the shared information from different tasks in the multi-task learning architecture in previous layers. Since the MOTS rankings for selecting the MTI-Net task-decoder head with the highest transfer learning performance were very accurate for the NYUD but not satisfying for the Pascal VOC dataset, further experiments need to varify the generalizability of MOTS rankings for the selection of the optimal task-decoder head from a multi-task architecture.
The pT-differential production cross sections of prompt and non-prompt (produced in beauty-hadron decays) D mesons were measured by the ALICE experiment at midrapidity (|y| < 0.5) in proton-proton collisions at s√ = 5.02 TeV. The data sample used in the analysis corresponds to an integrated luminosity of (19.3 ± 0.4) nb−1. D mesons were reconstructed from their decays D0 → K−π+, D+ → K−π+π+, and D+s→φπ+→K−K+π+ and their charge conjugates. Compared to previous measurements in the same rapidity region, the cross sections of prompt D+ and D+s mesons have an extended pT coverage and total uncertainties reduced by a factor ranging from 1.05 to 1.6, depending on pT, allowing for a more precise determination of their pT-integrated cross sections. The results are well described by perturbative QCD calculations. The fragmentation fraction of heavy quarks to strange mesons divided by the one to non-strange mesons, fs/(fu + fd), is compatible for charm and beauty quarks and with previous measurements at different centre-of-mass energies and collision systems. The bb¯¯¯ production cross section per rapidity unit at midrapidity, estimated from non-prompt D-meson measurements, is dσbb¯¯¯/dy∣∣|y|<0.5=34.5±2.4(stat)+4.7−2.9(tot.syst) μb. It is compatible with previous measurements at the same centre-of-mass energy and with the cross section pre- dicted by perturbative QCD calculations.
Future operation of the CBM detector requires ultra-fast analysis of the continuous stream of data from all subdetector systems. Determining the inter-system time shifts among individual detector systems in the existing prototype experiment mCBM is an essential step for data processing and in particular for stable data taking. Based on the input of raw measurements from all detector systems, the corresponding time correlations can be obtained at digital level by evaluating the differences in time stamps. If the relevant systems are stable during data taking and sufficient digital measurements are available, the distribution of time differences should display a clear peak. Up to now, the outcome of the processed time differences is stored in histograms and the maximum peak is considered, after the evaluation of all timeslices of a run leading to significant run times. The results presented here demonstrate the stability of the synchronicity of mCBM systems. Furthermore it is illustrated that relatively small amounts of raw measurements are sufficient to evaluate corresponding time correlations among individual mCBM detectors, thus enabling fast online monitoring of them in future online data processing.
We empirically investigate algorithms for solving Connected Components in the external memory model. In particular, we study whether the randomized O(Sort(E)) algorithm by Karger, Klein, and Tarjan can be implemented to compete with practically promising and simpler algorithms having only slightly worse theoretical cost, namely Borůvka’s algorithm and the algorithm by Sibeyn and collaborators. For all algorithms, we develop and test a number of tuning options. Our experiments are executed on a large set of different graph classes including random graphs, grids, geometric graphs, and hyperbolic graphs. Among our findings are: The Sibeyn algorithm is a very strong contender due to its simplicity and due to an added degree of freedom in its internal workings when used in the Connected Components setting. With the right tunings, the Karger-Klein-Tarjan algorithm can be implemented to be competitive in many cases. Higher graph density seems to benefit Karger-Klein-Tarjan relative to Sibeyn. Borůvka’s algorithm is not competitive with the two others.