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Die durch das Zweite Corona-Steuerhilfegesetz erfolgte Ausweitung des Verlustrücktrags ist dem Grunde nach ein hochgradig geeignetes und insbesondere breitenwirksames Mittel zur Stützung der Konjunktur. Das vorliegende Policy White Paper legt dar, dass allerdings Art und Umfang der gewählten Ausweitung unzureichend sind. Hierzu analysieren die Verfasser, wie sich die Ausweitung auf Unternehmen unterschiedlicher Größe und Rechtsform auswirkt. Auf Basis dieser Analyse zei-gen sie sodann, dass gemessen an den verfolgten konjunkturpolitischen Zielen es geboten gewesen wäre und weiterhin geboten ist, den Verlustrücktrag auf die Gewerbesteuer zu erstrecken.
Due to the resurrection of data-hungry models (such as deep convolutional neural nets), there is an increasing demand for large-scale labeled datasets and benchmarks in the computer vision fields (CV). However, collecting real data across diverse scene contexts along with high-quality annotations is often expensive and time-consuming, especially for detailed pixel-level label prediction tasks such as semantic segmentation, etc. To address the scarcity of real-world training sets, recent works have proposed the use of computer graphics (CG) generated data to train and/or characterize performance of modern CV systems. CG based virtual worlds provide easy access to ground truth annotations and control over scene states. Most of these works utilized training data simulated from video games and pre-designed virtual environments and demonstrated promising results. However, little effort has been devoted to the systematic generation of massive quantities of sufficiently complex synthetic scenes for training scene understanding algorithms. In this work, we develop a full pipeline for simulating large-scale datasets along with per-pixel ground truth information. Our simulation pipeline constitutes of mainly two components: (a) a stochastic scene generative model that automatically synthesizes traffic scene layouts by using marked point processes coupled with 3D CAD objects and factor potentials, (b) an annotated-image rendering tool that renders the sampled 3D scene as RGB image with a chosen rendering method along with pixel-level annotations such as semantic labels, depth, surface normals etc. This pipeline is capable of automatically generating and rendering a potentially infinite variety of outdoor traffic scenes that can be used to train convolutional neural nets (CNN).
However, several recent works, including our own initial experiments demonstrated that the CV models that are trained naively on simulated data lack generalization capabilities to real-world scenes. This opens up several fundamental questions about what is it lacking in simulated data compared to real data and how to use it effectively. Furthermore, there has been a long debate since 1980’s on the usefulness of CG generated data for tuning CV systems. Primarily, the impact of modeling errors and computational rendering approximations, due to various choices in the rendering pipeline, on trained CV systems generalization performance is still not clear. In this thesis, we take a case study in the context of traffic scenarios to empirically analyze the performance degradations when CV systems trained with virtual data are transferred to real data. We first explore system performance tradeoffs due to the choice of the rendering engine (e.g., Lambertian shader (LS), ray-tracing (RT), and Monte-Carlo path tracing (MCPT)) and their parameters. A CNN architecture, DeepLab, that performs semantic segmentation, is chosen as the CV system being evaluated. In our case study, involving traffic scenes, a CNN trained with CG data samples generated with photorealistic rendering methods (such as RT or MCPT), shows already a reasonably good performance on real-world testing data from CityScapes benchmark. Use of samples from an elementary rendering method, i.e., LS, degraded the performance of CNN by nearly 20%. This result conveys that training data must be photorealistic enough for better generalizability of the trained CNN models. Furthermore, the use of physics-based MCPT rendering improved the performance by 6% but at the cost of more than three times the rendering time. This MCPT generated dataset when augmented with just 10% of real-world training data from CityScapes dataset, the performance levels achieved are comparable to that of training CNN with the complete CityScapes dataset.
The next aspect we study in the thesis involves the impact of choice of parameter settings of scene generation model on the generalization performance of CNN models trained with the generated data. Towards this end, we first propose an algorithm to estimate our scene generation model parameters given an unlabeled real world dataset from the target domain. This unsupervised tuning approach utilizes the concept of generative adversarial training, which aims at adapting the generative model by measuring the discrepancy between generated and real data in terms of their separability in the space of a deep discriminatively-trained classifier. Our method involves an iterative estimation of the posterior density of prior distributions for the generative graphical model used in the simulation. Initially, we assume uniform distributions as priors over parameters of a scene described by our generative graphical model. As iterations proceed the uniform prior distributions are updated sequentially to distributions for the simulation model parameters that leads to simulated data with statistics that are closer to the distributions of the unlabeled target data.
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Software updates are a critical success factor in mobile app ecosystems. Through publishing regular updates, platform providers enhance their operating systems for the benefit of both end users and third-party developers. It is also a way of attracting new customers. However, this platform evolution poses the risk of inadvertently introducing software problems, which can severely disturb the ecosystem’s balance by compromising its foundational technologies. So far, little to no research has addressed this issue from a user-centered perspective. The thesis at hand draws on IS post-adoption literature to investigate the potential negative influences of operating system updates on mobile app users. The release of Apple’s iOS 13 update serves as research object. Based on over half a million user reviews from the AppStore, data mining techniques are applied to study the impact of the new platform version. The results show that iOS 13 caused complications with a large number of popular apps, leading to a significant decline in user ratings and an uptrend in negative sentiment. Feature requests, functional complaints, and device compatibility are identified as the three major issue categories. These issue types are compared in terms of their quantifiable negative effect on users’ continuance intention. In essence, the findings contribute to IS research on post-adoption behavior and provide guidance to ecosystem participants in dealing with update-induced platform issues.
The work of this thesis focuses on the targeting of G-quadruplexes (G4s), wherein several specific and potential ligands were designed, synthesized and characterized for its structural and biological activity. G4s are nucleic acid secondary structures that may form in single-stranded guanine (G)-rich sequences under physiological conditions. Four Guanines (Gs) bind via Hoogsteen-type hydrogen bonds base pairing to yield G-quartets, which in turn stack on top of each other to form the G4. G4s are highly polymorphic, both in terms of strand stoichiometry (forming both inter and intramolecular structures) and strand orientation/topology. The presence of K+ cations specifically supports G4 formation and stability. In the human genome G4 DNA motifs have been found in telomeres, G-rich micro and mini-satellites, up-stream to oncogene promoters and within the ribosomal DNA (rDNA). Human G4 DNA motifs are over-expressed in recombinogenic regions, which are associated with genomic damage in cancer cells.
In the present work, we focus on lead identification with specificity towards the c-MYC promoter G4s. Drug discovery is a highly time consuming and costly process. Lead identification and development are key steps in the drug discovery program. Studies have suggested that a large number of commercially available drugs exhibit deep structural similarity to the lead compounds from which they were developed. Quality lead identification in terms of compounds with high potency and selectivity, favorable physicochemical parameters and in vitro Absorption Distribution Metabolism and Excretion (ADME) parameters are the foremost requirements for the success of the drug discovery process. We herein describe the fragment-based drug design approach for the development of pyrrolidine-substituted 5-nitroindole derivatives as a new class of G4 ligands that exhibit high affinity and selectivity for the c-MYC promoter G-quadruplex. This chapter focuses on the methodology explored whilst finding a suitable hit and its optimization with fragment expansion strategies which undergo efficient G4 binding.
To target G4 DNA, screenings of numerous heterocycles have been reported including indoles, 7-azaindoles, 1H-indazol-3-yl, benzothiazole, imidazo[1,5-a]pyridine, 2,6- diaminopyrimidin-4-ol, 1H-pyrazolo[4,3 d]pyrimidin-7-amine, morpholino, bis-indoles, 2-hydroxynaphthalene-1,4-dione, 1,4-dihydroxyanthracene-9,10-dione, benzofuran and piperonal derived from several alkaloids. In this part of the thesis, we set out to identify new binders targeting the c-MYC G-quadruplex starting from the indole fragment. Several synthetic strategies are reported to optimize and generate best hits starting from 5-nitro indole derivatives by introducing the secondary cationic linked pyrrolidine side chain. Interestingly, all improved versions of G4-indole fragments 5, 7 and 12 contain this 5-nitro functionality, which may aid in the electrostatic binding and contributes to hydrogen binding interactions of the ligands to G4 DNA. In-silico drug design, biological and biophysical analyses illustrated that the substituted 5-nitro indoles scaffolds show preferential affinity towards the c-MYC promoter G-quadruplex compared to other G-quadruplexes and double stranded DNA. In vitro cellular studies confirm that the substituted indole scaffolds downregulate c-MYC expression in cancer cells and have the potential to induce cell cycle arrest in the G0/G1 phase. NMR analysis suggests that 5, 7, and 12 interacts in a fast exchange regime with the terminal G-quartets (5’ and 3’end) in a 2:1 stoichiometry.
To further optimize the fragment generated in chapter II, a novel series of triazole linked indole derivatives as a potential G quadruplex stabilizers have been described in chapter III. The potential ligands can be obtained through an efficient, convergent, synthetic route in moderate to good yields. The synthesized triazole linked indole derivatives are selective towards c- MYC G4-DNA vs. duplex-DNA. The planarity of the aromatic core and its ability to occupy more surface area by stacking over the G4 greatly affect the ability of the compounds to stabilize the G4. Further biophysical and biological studies revealed that the triazole linked nitro indoles are more promising than the amino indole derivatives.
Additionally, the importance of the nitro functional group has been justified by molecular docking studies, where hydrogen-bonding interactions were observed in between the nitro group and the G4 base pairs of the G-quadruplex. In biological findings, most of the synthesized triazole linked nitro indoles has found to be effective against human carcinoma (cervical) HeLa cell lines. Furthermore, western blot and cell cycle analysis confirms that the novel triazole linked 5-nitro indole derivatives (9b) could down-regulate c-MYC oncogene expression in cancer cells via stabilizing its promoter quadruplex structure, arresting cell cycle in G0/G1 phase. NMR analysis suggests that 9b interacts in slow exchange regime with the terminal G-quartets (5’ and 3’-end).
In chapter IV of the thesis, we have developed the synthetic strategies to generate more potent G4 ligands via Knoevenagel condensation. To investigate novel and selective G4 ligands for cancer chemotherapy, we designed and synthesized a series of azaindolin-2-one derivatives (11, 14, 15, 16 and 22) by attaching cationic pyrrolidine side chains and introducing a fluorine atom into the aromatic chromophore (Fig. 3). Fluorine atoms, with high electronegativity and small size, often exhibit unique properties in functional molecules. The electron-withdrawing effect of fluorine could reduce the electron density of the aromatic chromophore, which might favor a stronger interaction with the electron-rich π-system of the G-quartet. In addition, the introduction of fluorine atoms into small molecules might improve lipophilicity and thus the bioavailability. Fluorescent indicator displacement assay (FID) assays suggests that the synthesized azaindolin-2-one derivatives are selective towards c-MYC G4-DNA vs. duplex-DNA and showed potent anticancer activity against human carcinoma (cervical) HeLa cell lines. They down-regulate c-MYC expression in cancer cells via stabilizing its promoter quadruplex structure, arresting cell cycle in G0/G1 phase. Furthermore, NMR spectroscopy suggests that azaindolin-2-one conjugate interacts with terminal G-quartets as well as with the nearby G-rich tract (G13-G14-G15 and G8-G9-G10) of c-MYC quadruplex in intermediate exchange regime.
Cortical circuits exhibit highly dynamic and complex neural activity. Intriguingly, cortical activity exhibits consistently two key features across observed species and brain areas. First, individual neurons tend to be co-active in spatially localized domains forming orderly arranged, modular layouts with a typical spatial scale. Second, cortical elements are correlated in their activity over large distances reflecting long-range network interactions distributed over several millimeters. Currently, it is unclear how these two fundamental properties emerge in the early developing cortical activity.
Here, I aim to fill this gap by combining analyses of chronic imaging data and network models of developing cortical activity. Neural recordings of spontaneous and visually evoked activity in primary visual cortex of ferrets during their early cortical development were obtained using in vivo 2-photon and widefield epi-fluorescence calcium imaging. Spontaneous activity was used to probe the early state of cortical networks as its spatiotemporal organization is independent of a stimulus-imposed structure, and it is already present early in cortical development prior to reliably evoked responses. To assess the mature functional organization of distributed networks in cortex, the tuning of neural responses to stimulus features, in particular to the orientation of an edge-like stimulus, was assessed. Cortical responses to moving gratings of varying orientations form an orderly arranged layout of orientation domains extending over several millimeters.
To begin with, I showed that spontaneous activity correlations extend over several millimeters, supporting the assumption of using spontaneous activity to assess distributed networks in cortex.
Next, I asked how distributed networks in the mature visual cortex - assessed by spontaneous activity correlations - are related to its fine-scale functional organization. I found that the spatially extended and modular spontaneous correlation patterns accurately predict the fine spatial structure of visually evoked orientation domains several millimeters away. These results suggest a close relation between spontaneous correlations and visually evoked responses on a fine spatial scale and across large spatial distances.
As the principles governing the functional organization and development of distributed network interactions in the neocortex remain poorly understood, I next asked how long range correlated activity arises early in development. I found that key features of mature spontaneous activity introduced in this work, including long-range spontaneous correlations, were present already early in cortical development prior to the maturation of long-range, horizontal connections, and the predicted mature orientation preference layout. Even after silencing feed-forward input drive by inactivating retina or thalamus, long-range correlated and modular activity robustly emerged in early cortex. These results suggest that local recurrent connections in early cortical circuits can generate structured long-range network correlations that guide the formation of visually-evoked distributed functional networks.
To investigate how these large-scale cortical networks emerge prior to the maturation and elaboration of long-range horizontal connectivity, I examined a statistical network model describing an ensemble of spatially extended spontaneous activity patterns. I found a direct relationship between the dimensionality of this ensemble of activity patterns and the decay of its correlation structure. Specifically, reducing the dimensionality of the ensemble leads to an increase in the spatial range of the correlation structure.
To test whether this mechanism could generate a long-range correlation structure in cortical circuits, I studied a dynamical network model implementing a dimensionality reduction mechanism. Based on previous work demonstrating that network heterogeneity reduces the dimensionality of activity patterns, I showed that by increasing the degree of heterogeneity in the network, the dimensionality of the ensemble of activity patterns decreases and in turn their correlations extend over a greater range. A comparison to experimental data revealed a quantitative match between the network model and the observations in vivo in several of the key features of the early cortex including the spatial scale of correlations. Low dimensionality of spontaneous activity thus might provide an organizational principle explaining the observed long-range correlation structure in the early cortex.
Finally, I asked whether a network with a biologically plausible architecture can generate modular activity. Several classical models showed that modular activity patterns can emerge via an intracortical mechanism involving lateral inhibition. However, this assumption appears to be in conflict with current experimental evidence. Moreover, these network models were not experimentally tested, so far. Here, I showed by using linear stability analysis that spatially localized self-inhibition relaxes the constraints on the connectivity structure in a network model, such that biologically more plausible network motifs with shorter ranging inhibition than excitation can robustly generate modular activity.
Importantly, I also provided several model predictions to make the class of network models experimentally testable in view of recent technological advancements in imaging and manipulation of cortical circuits. A critical prediction of the model is the decrease in spacing of active domains when the total amount of inhibition increases. These results provide a novel mechanism of how cortical circuits with short-range inhibition can form modular activity.
Taken together, this thesis provides evidence that the two described fundamental features of neural activity are already present in the early cortex and shows that activity with those features can be generated in network models with an architecture consistent with the early cortex using basic principles.
Cerebral lesions may cause degeneration and neuroplastic reorganization in both the ipsi- and the contralesional hemisphere, presumably creating an imbalance of primarily inhibitory interhemispheric influences produced via transcallosal pathways. The two hemispheres are thought to mutually hamper neuroplastic reorganization of the other hemisphere. The results of preceding degeneration and neuroplastic reorganization of white matter may be reflected by Diffusion Tensor Imaging-derived diffusivity parameters such as fractional anisotropy (FA). In this study, we applied Diffusion Tensor Imaging (DTI) to contrast the white matter status of the contralesional hemisphere of young lesioned brains with and without contralateral influences by comparing patients after hemispherotomy to those who had not undergone neurosurgery. DTI was applied to 43 healthy controls (26 females, mean age ± SD: 25.07 ± 11.33 years) and two groups of in total 51 epilepsy patients with comparable juvenile brain lesions (32 females, mean age ± SD: 25.69 ± 12.77 years) either after hemispherotomy (30 of 51 patients) or without neurosurgery (21 of 51 patients), respectively. FA values were compared between these groups using the unbiased tract-based spatial statistics approach. A voxel-wise ANCOVA controlling for age at scan yielded significant group differences in FA. A post hoc t-test between hemispherotomy patients and healthy controls revealed widespread supra-threshold voxels in the contralesional hemisphere of hemispherotomy patients indicating comparatively higher FA values (p < 0.05, FWE-corrected). The non-surgery group, in contrast, showed extensive supra-threshold voxels indicating lower FA values in the contralesional hemisphere as compared to healthy controls (p < 0.05, FWE-corrected). Whereas lower FA values are suggestive of pronounced contralesional degeneration in the non-surgery group, higher FA values in the hemispherotomy group may be interpreted as a result of preceding plastic remodeling. We conclude that, whether juvenile brain lesions are associated with contralesional degeneration or reorganization partly depends on the ipsilesional hemisphere. Contralesional reorganization as observed in hemispherotomy patients was most likely enabled by the complete neurosurgical deafferentation of the ipsilesional hemisphere and, thereby, the disinhibition of the neuroplastic potential of the contralesional hemisphere. The main argument of this study is that hemispherotomy may be seen as a major plastic stimulus and as a prerequisite for contralesional neuroplastic remodeling in patients with juvenile brain lesions.