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This article explores various attempts to critique the law with reference to an authority or idea that is seen as transcending law in its existing forms. As heuristic tools, I use a distinction be-tween prophetic and apocalyptic discourses, the former referring to discourses that remain scep-tical to the possibility of suspending law in any absolute sense; the latter describing discourses that art
This article probes the writings of the Jewish Trotskyist thinker Daniel Bensaïd (1946–2010) in light of recent debates on political theology. In contrast to what is sometimes explicitly referred to as ‘apocalyptic political theology’, it makes a case for what may be described as a ‘prophetic political theology’. Yet it is not obvious to claim Bensaïd as a proponent for such a project, since he ex
We present MubyNet - a feed-forward, multitask, bottom up system for the integrated localization, as well as 3d pose and shape estimation, of multiple people in monocular images. The challenge is the formal modeling of the problem that intrinsically requires discrete and continuous computation, e.g. grouping people vs. predicting 3d pose. The model identifies human body structures (joints and limb
BACKGROUND: Low level of physical activity is a risk factor for new cardiac events in out-of-hospital cardiac arrest (OHCA) survivors. Physical activity can be assessed by self-reporting or objectively by accelerometery.AIM: To investigate the agreement between self-reported and objectively assessed physical activity among OHCA survivors HYPOTHESIS: Self-reported levels of physical activity will s
Global climate models (GCMs) are challenged by difficulties in simulating cloud phase and cloud radiative effect over the Southern Ocean (SO). Some of the new-generation GCMs predict too much liquid and too little ice in mixed-phase clouds. This misrepresentation of cloud phase in GCMs results in weaker negative cloud feedback over the SO and a higher climate sensitivity. Based on a model comparis
Despite of having no explicit shape model, multi-atlas approaches to image segmentation have proved to be a top-performer for several diverse datasets and imaging modalities. In this paper, we show how one can directly incorporate shape regularization into the multi-atlas framework. Unlike traditional multi-atlas methods, our proposed approach does not rely on label fusion on the voxel level. Inst
In this paper we study the problem of estimating receiver and sender positions using time-difference-of-arrival measurements. For this, we use a stratified, two-tiered approach. In the first step the problem is converted to a low-rank matrix estimation problem. We present new, efficient solvers for the minimal problems of this low-rank problem. These solvers are used in a hypothesis and test manne
The decomposition of nonlinear chirp modes is a challenging task, typically requiring prior knowledge of the number of modes a signal contains. In this work, we present a greedy nonlinear chirp mode estimation (NCME) technique that forms the used decomposition basis from the signal itself, using an arctangent demodulation technique. The resulting decomposition is formed by considering the residual
pTropospheric ozone concentrations are sensitive to natural emissions of precursor compounds. In contrast to existing assumptions, recent evidence indicates that terrestrial vegetation emissions in the pre-industrial era were larger than in the present day. We use a chemical transport model and a radiative transfer model to show that revised inventories of pre-industrial fire and biogenic emission
Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. It can be challenging to estimate the full cross-covariance matrix for a temporal hierarchy, which can easily be of very large dimension, yet it is difficult to know a
Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structure
Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Un
Millimeter-wave cellular communication requires beamforming procedures that enable alignment of the transmitter and receiver beams as the user equipment (UE) moves. For efficient beam tracking it is advantageous to classify users according to their traffic and mobility patterns. Research to date has demonstrated efficient ways of machine learning based UE classification. Although different machine
The learning with errors (LWE) problem is one of the main mathematical foundations of post-quantum cryptography. One of the main groups of algorithms for solving LWE is the Blum–Kalai–Wasserman (BKW) algorithm. This paper presents new improvements of BKW-style algorithms for solving LWE instances. We target minimum concrete complexity, and we introduce a new reduction step where we partially reduc
In this paper we consider a collection of relative pose problems which arise naturally in applications for visual indoor navigation using unmanned aerial vehicles (UAVs). We focus on cases where additional information from an onboard IMU is available and thus provides a partial extrinsic calibration through the gravitational vector. The solvers are designed for a partially calibrated camera, for a
In this paper, we present a side-channel attack on a first-order masked implementation of IND-CCA secure Saber KEM. We show how to recover both the session key and the long-term secret key from 24 traces using a deep neural network created at the profiling stage. The proposed message recovery approach learns a higher-order model directly, without explicitly extracting random masks at each executio
Beamforming technology plays a significant role in source localization and quantification. As traditional delay-and-sum beamformers generally yield low spatial resolution, as well as suffer from the occurrence of spurious sources, different forms of deconvolution methods have been proposed in the literature. In this work, we propose two approaches based on a sparse reconstruction framework combine
This paper investigates the stabilization of underactuated vehicles moving in a three-dimensional vector space. The vehicle's model is established on the matrix Lie group SE(3), which describes the configuration of rigid bodies globally and uniquely. We focus on the kinematic model of the underactuated vehicle, which features an underactuation form that has no sway and heave velocity. To compensat
