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In this essay I argue that while research in Jewish studies over the last several decades has done much to erode the historical narrative of Jewish/non-Jewish separation and detachment, it has also raised various questions pertaining to the outcome of Jewish/non-Jewish interactions and coexistence as well as the contours of Jewish difference. I contend that employing the concepts of conviviality, ethnic/religious/national indifference, and similarity will greatly facilitate answering these questions.
Habsburg Central Europe
(2024)
Central Europe is characterized by linguistic and cultural density as well as by endogenous and exogenous cultural influences. These constellations were especially visible in the former Habsburg Empire, where they influenced the formation of individual and collective identities. This led not only to continual crises and conflicts, but also to an equally enormous creative potential as became apparent in the culture of the fin-de-siècle.
At the junction of greenhouse and icehouse climate states, the Eocene-Oligocene Transition (EOT) is a key moment in Cenozoic climate history. While it is associated with severe extinctions and biodiversity turnovers on land, the role of terrestrial climate evolution remains poorly resolved, especially the associated changes in seasonality. Some paleobotanical and geochemical continental records in parts of the Northern Hemisphere suggest the EOT is associated with a marked cooling in winter, leading to the development of more pronounced seasons (i.e., an increase in the mean annual range of temperature, MATR). However, the MATR increase has been barely studied by climate models and large uncertainties remain on its origin, geographical extent and impact. In order to better understand and describe temperature seasonality changes between the middle Eocene and the early Oligocene, we use the Earth system model IPSL-CM5A2 and a set of simulations reconstructing the EOT through three major climate forcings: pCO(2) decrease (1120, 840 and 560 ppm), the Antarctic ice-sheet (AIS) formation and the associated sea-level decrease. Our simulations suggest that pCO(2) lowering alone is not sufficient to explain the seasonality evolution described by the data through the EOT but rather that the combined effects of pCO(2) , AIS formation and increased continentality provide the best data-model agreement.pCO(2) decrease induces a zonal pattern with alternating increasing and decreasing seasonality bands particularly strong in the northern high latitudes (up to 8 degrees C MATR increase) due to sea-ice and surface albedo feedback. Conversely, the onset of the AIS is responsible for a more constant surface albedo yearly, which leads to a strong decrease in seasonality in the southern midlatitudes to high latitudes (> 40 degrees S). Finally, continental areas that emerged due to the sea-level lowering cause the largest increase in seasonality and explain most of the global heterogeneity in MATR changes (1MATR) patterns. The Delta MATR patterns we reconstruct are generally consistent with the variability of the EOT biotic crisis intensity across the Northern Hemisphere and provide insights on their underlying mechanisms.
We present a detailed spectroscopic and timing analysis of X-ray observations of the bright pulsar PSR B0656+14. The observations were obtained simultaneously with eROSITA and XMM-Newton during the calibration and performance verification phase of the Spektrum-Roentgen-Gamma mission (SRG). The analysis of the 100 ks deep observation of eROSITA is supported by archival observations of the source, including XMM-Newton, NuSTAR, and NICER. Using XMM-Newton and NICER, we first established an X-ray ephemeris for the time interval 2015 to 2020, which connects all X-ray observations in this period without cycle count alias and phase shifts. The mean eROSITA spectrum clearly reveals an absorption feature originating from the star at 570 eV with a Gaussian sigma of about 70 eV that was tentatively identified in a previous long XMM-Newton observation. A second previously discussed absorption feature occurs at 260-265 eV and is described here as an absorption edge. It could be of atmospheric or of instrumental origin. These absorption features are superposed on various emission components that are phenomenologically described here as the sum of hot (120 eV) and cold (65 eV) blackbody components, both of photospheric origin, and a power law with photon index Gamma = 2 from the magnetosphere. We created energy-dependent light curves and phase-resolved spectra with a high signal-to-noise ratio. The phase-resolved spectroscopy reveals that the Gaussian absorption line at 570 eV is clearly present throughout similar to 60% of the spin cycle, but it is otherwise undetected. Likewise, its parameters were found to be dependent on phase. The visibility of the line strength coincides in phase with the maximum flux of the hot blackbody. If the line originates from the stellar surface, it nevertheless likely originates from a different location than the hot polar cap. We also present three families of model atmospheres: a magnetized atmosphere, a condensed surface, and a mixed model. They were applied to the mean observed spectrum, whose continuum fit the observed data well. The atmosphere model, however, predicts distances that are too short. For the mixed model, the Gaussian absorption may be interpreted as proton cyclotron absorption in a field as high as 10(14) G, which is significantly higher than the field derived from the moderate observed spin-down.
Am Ende der Globalisierung
(2021)
Die Globalisierung ist zur allgegenwärtigen Gewissheit geworden. Doch wie zutreffend ist das Konzept »Globalisierung«, wenn zeitgleich nationale Grenzen gestärkt und transnationale Freihandelszonen ausgeweitet werden, wenn auf unterschiedlichen scales Territorien überwunden und zugleich territoriale Abgrenzungen neu gesetzt werden? Aktuelle Veränderungen als Re-Figuration von Räumen zu verstehen, ermöglicht die Analyse und Diskussion widersprüchlicher, spannungsreicher und konflikthafter räumlicher Prozesse und ihrer alltäglichen Erfahrung. Die interdisziplinären Beiträge des Bandes liefern theoretische und empirische Analysen zu politischen, digitalen und alltäglichen Räumen im Konzept der Re-Figuration.
Zimzum
(2023)
Zimzum is the kabbalistic idea that God created the world by limiting his omnipresence. Zimzum originated in the teachings of the sixteenth-century Jewish mystic Isaac Luria and here, Christoph Schulte follows its traces across the Jewish and Christian intellectual history of Europe and North America over four centuries.
The Hebrew word zimzum originally means “contraction,” “withdrawal,” “retreat,” “limitation,” and “concentration.” In Kabbalah, zimzum is a term for God’s self-limitation, done before creating the world to create the world. Jewish mystic Isaac Luria coined this term in Galilee in the sixteenth century, positing that the God who was “Ein-Sof,” unlimited and omnipresent before creation, must concentrate himself in the zimzum and withdraw in order to make room for the creation of the world in God’s own center. At the same time, God also limits his infinite omnipotence to allow the finite world to arise. Without the zimzum there is no creation, making zimzum one of the basic concepts of Judaism.
The Lurianic doctrine of the zimzum has been considered an intellectual showpiece of the Kabbalah and of Jewish philosophy. The teaching of the zimzum has appeared in the Kabbalistic literature across Central and Eastern Europe, perhaps most famously in Hasidic literature up to the present day and in philosopher and historian Gershom Scholem’s epoch-making research on Jewish mysticism. The Zimzum has fascinated Jewish and Christian theologians, philosophers, and writers like no other Kabbalistic teaching. This can be seen across the philosophy and cultural history of the twentieth century as it gained prominence among such diverse authors and artists as Franz Rosenzweig, Hans Jonas, Isaac Bashevis Singer, Harold Bloom, Barnett Newman, and Anselm Kiefer.
This book follows the traces of the zimzum across the Jewish and Christian intellectual history of Europe and North America over more than four centuries, where Judaism and Christianity, theosophy and philosophy, divine and human, mysticism and literature, Kabbalah and the arts encounter, mix, and cross-fertilize the interpretations and appropriations of this doctrine of God’s self-entanglement and limitation.
Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models
(2023)
Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications.
Deep learning has seen widespread application in many domains, mainly for its ability to learn data representations from raw input data. Nevertheless, its success has so far been coupled with the availability of large annotated (labelled) datasets. This is a requirement that is difficult to fulfil in several domains, such as in medical imaging. Annotation costs form a barrier in extending deep learning to clinically-relevant use cases. The labels associated with medical images are scarce, since the generation of expert annotations of multimodal patient data at scale is non-trivial, expensive, and time-consuming. This substantiates the need for algorithms that learn from the increasing amounts of unlabeled data. Self-supervised representation learning algorithms offer a pertinent solution, as they allow solving real-world (downstream) deep learning tasks with fewer annotations. Self-supervised approaches leverage unlabeled samples to acquire generic features about different concepts, enabling annotation-efficient downstream task solving subsequently.
Nevertheless, medical images present multiple unique and inherent challenges for existing self-supervised learning approaches, which we seek to address in this thesis: (i) medical images are multimodal, and their multiple modalities are heterogeneous in nature and imbalanced in quantities, e.g. MRI and CT; (ii) medical scans are multi-dimensional, often in 3D instead of 2D; (iii) disease patterns in medical scans are numerous and their incidence exhibits a long-tail distribution, so it is oftentimes essential to fuse knowledge from different data modalities, e.g. genomics or clinical data, to capture disease traits more comprehensively; (iv) Medical scans usually exhibit more uniform color density distributions, e.g. in dental X-Rays, than natural images. Our proposed self-supervised methods meet these challenges, besides significantly reducing the amounts of required annotations.
We evaluate our self-supervised methods on a wide array of medical imaging applications and tasks. Our experimental results demonstrate the obtained gains in both annotation-efficiency and performance; our proposed methods outperform many approaches from related literature. Additionally, in case of fusion with genetic modalities, our methods also allow for cross-modal interpretability. In this thesis, not only we show that self-supervised learning is capable of mitigating manual annotation costs, but also our proposed solutions demonstrate how to better utilize it in the medical imaging domain. Progress in self-supervised learning has the potential to extend deep learning algorithms application to clinical scenarios.