The Emerging Role of E-Agribusiness - State of the Art and Perspectives in Germany

The aim of this paper is to describe and analyse the emerging role of electronic agribusiness and the corresponding consequences in Germany. The study was carried out as a consecutive step by step procedure, including empirical surveys in farm and agribusiness small and medium size enterprises, accompanied by case studies in agribusiness firms, and a technology assessment expert survey. The results indicate that internet penetration on farm and agribusiness enterprise levels is rather high, but currently information and communication activities clearly dominate over commercial business activities. The case studies show that the internal communication changed significantly due to the implementation of internet applications like emailing, newsgroups, periodic information sheets and video-conferencing. Additionally, external communication changes according to the availability of internet features, e.g. suppliers of parts established internet networks for co-ordinated sales activities. The technology assessment expert survey indicates a gain in importance for pre- and after-sales services and communication activities by using internet technologies. Conclusions are - among others - an increasing importance of e-agribusiness and correspondingly an acceleration of structural change, but low or no expected effects on ecological parameters and on the labour market.E-Business, E-Agribusiness, Technology assessment, Survey, Germany, Agribusiness,

GABA Metabolism and Transport: Effects on Synaptic Efficacy

GABAergic inhibition is an important regulator of excitability in neuronal networks. In addition, inhibitory synaptic signals contribute crucially to the organization of spatiotemporal patterns of network activity, especially during coherent oscillations. In order to maintain stable network states, the release of GABA by interneurons must be plastic in timing and amount. This homeostatic regulation is achieved by several pre- and postsynaptic mechanisms and is triggered by various activity-dependent local signals such as excitatory input or ambient levels of neurotransmitters. Here, we review findings on the availability of GABA for release at presynaptic terminals of interneurons. Presynaptic GABA content seems to be an important determinant of inhibitory efficacy and can be differentially regulated by changing synthesis, transport, and degradation of GABA or related molecules. We will discuss the functional impact of such regulations on neuronal network patterns and, finally, point towards pharmacological approaches targeting these processes

The Weak Call-By-Value {\lambda}-Calculus is Reasonable for Both Time and Space

We study the weak call-by-value $\lambda$-calculus as a model for computational complexity theory and establish the natural measures for time and space -- the number of beta-reductions and the size of the largest term in a computation -- as reasonable measures with respect to the invariance thesis of Slot and van Emde Boas [STOC~84]. More precisely, we show that, using those measures, Turing machines and the weak call-by-value $\lambda$-calculus can simulate each other within a polynomial overhead in time and a constant factor overhead in space for all computations that terminate in (encodings) of 'true' or 'false'. We consider this result as a solution to the long-standing open problem, explicitly posed by Accattoli [ENTCS~18], of whether the natural measures for time and space of the $\lambda$-calculus are reasonable, at least in case of weak call-by-value evaluation. Our proof relies on a hybrid of two simulation strategies of reductions in the weak call-by-value $\lambda$-calculus by Turing machines, both of which are insufficient if taken alone. The first strategy is the most naive one in the sense that a reduction sequence is simulated precisely as given by the reduction rules; in particular, all substitutions are executed immediately. This simulation runs within a constant overhead in space, but the overhead in time might be exponential. The second strategy is heap-based and relies on structure sharing, similar to existing compilers of eager functional languages. This strategy only has a polynomial overhead in time, but the space consumption might require an additional factor of $\log n$, which is essentially due to the size of the pointers required for this strategy. Our main contribution is the construction and verification of a space-aware interleaving of the two strategies, which is shown to yield both a constant overhead in space and a polynomial overhead in time

The weak call-by-value λ-calculus is reasonable for both time and space

We study the weak call-by-value -calculus as a model for computational complexity theory and establish the natural measures for time and space Ð the number of beta-reduction steps and the size of the largest term in a computation Ð as reasonable measures with respect to the invariance thesis of Slot and van Emde Boas from 1984. More precisely, we show that, using those measures, Turing machines and the weak call-by-value -calculus can simulate each other within a polynomial overhead in time and a constant factor overhead in space for all computations terminating in (encodings of) łtruež or łfalsež. The simulation yields that standard complexity classes like , NP, PSPACE, or EXP can be defined solely in terms of the -calculus, but does not cover sublinear time or space. Note that our measures still have the well-known size explosion property, where the space measure of a computation can be exponentially bigger than its time measure. However, our result implies that this exponential gap disappears once complexity classes are considered instead of concrete computations. We consider this result a first step towards a solution for the long-standing open problem of whether the natural measures for time and space of the -calculus are reasonable. Our proof for the weak call-by-value -calculus is the first proof of reasonability (including both time and space) for a functional language based on natural measures and enables the formal verification of complexity-theoretic proofs concerning complexity classes, both on paper and in proof assistants. The proof idea relies on a hybrid of two simulation strategies of reductions in the weak call-by-value -calculus by Turing machines, both of which are insufficient if taken alone. The first strategy is the most naive one in the sense that a reduction sequence is simulated precisely as given by the reduction rules; in particular, all substitutions are executed immediately. This simulation runs within a constant overhead in space, but the overhead in time might be exponential. The second strategy is heap-based and relies on structure sharing, similar to existing compilers of eager functional languages. This strategy only has a polynomial overhead in time, but the space consumption might require an additional factor of log, which is essentially due to the size of the pointers required for this strategy. Our main contribution is the construction and verification of a space-aware interleaving of the two strategies, which is shown to yield both a constant overhead in space and a polynomial overhead in time

Defect healing at room temperature in pentacene thin films and improved transistor performance

We report on a healing of defects at room temperature in the organic semiconductor pentacene. This peculiar effect is a direct consequence of the weak intermolecular interaction which is characteristic of organic semiconductors. Pentacene thin-film transistors were fabricated and characterized by in situ gated four-terminal measurements. Under high vacuum conditions (base pressure of order 10E-8 mbar), the device performance is found to improve with time. The effective field-effect mobility increases by as much as a factor of two and mobilities up to 0.45 cm2/Vs were achieved. In addition, the contact resistance decreases by more than an order of magnitude and there is a significant reduction in current hysteresis. Oxygen/nitrogen exposure and annealing experiments show the improvement of the electronic parameters to be driven by a thermally promoted process and not by chemical doping. In order to extract the spectral density of trap states from the transistor characteristics, we have implemented a powerful scheme which allows for a calculation of the trap densities with high accuracy in a straightforward fashion. We show the performance improvement to be due to a reduction in the density of shallow traps <0.15 eV from the valence band edge, while the energetically deeper traps are essentially unaffected. This work contributes to an understanding of the shallow traps in organic semiconductors and identifies structural point defects within the grains of the polycrystalline thin films as a major cause.Comment: 13 pages, 13 figures, to be published in Phys. Rev.

Quality of life in a dynamic spatial model

We develop a dynamic spatial model in which heterogeneous workers are imperfectly mobile and forward-looking and yet all structural fundamentals can be inverted without assuming that the economy is in a stationary spatial equilibrium. Exploiting this novel feature of the model, we show that the canonical spatial equilibrium framework understates spatial quality of-life differentials, the urban quality-of-life premium and the value of local non-marketed goods. Unlike the canonical spatial equilibrium framework, the model quantitatively accounts for local welfare effects that motivate many place-based policies seeking to improve quality of life

Lockdown shows us it is not work that attracts us to big cities – but the social life

COVID’s effect on the social lives of city dwellers – being able to meet at the pub, restaurants or theatre – may be more relevant for the future of cities than its impact on work, write Gabriel Ahlfeldt, Fabian Bald, Duncan Roth, and Tobias Seidel

Parametrised polyconvex hyperelasticity with physics-augmented neural networks

In the present work, neural networks are applied to formulate parametrised hyperelastic constitutive models. The models fulfill all common mechanical conditions of hyperelasticity by construction. In particular, partially input-convex neural network (pICNN) architectures are applied based on feed-forward neural networks. Receiving two different sets of input arguments, pICNNs are convex in one of them, while for the other, they represent arbitrary relationships which are not necessarily convex. In this way, the model can fulfill convexity conditions stemming from mechanical considerations without being too restrictive on the functional relationship in additional parameters, which may not necessarily be convex. Two different models are introduced, where one can represent arbitrary functional relationships in the additional parameters, while the other is monotonic in the additional parameters. As a first proof of concept, the model is calibrated to data generated with two differently parametrised analytical potentials, whereby three different pICNN architectures are investigated. In all cases, the proposed model shows excellent performance

Complementary tuning of Na+ and K+ channel gating underlies fast and energy-efficient action potentials in GABAergic interneuron axons

Fast-spiking, parvalbumin-expressing GABAergic interneurons (PV+-BCs) express a complex machinery of rapid signaling mechanisms, including specialized voltage-gated ion channels to generate brief action potentials (APs). However, short APs are associated with overlapping Na+ and K+ fluxes and are therefore energetically expensive. How the potentially vicious combination of high AP frequency and inefficient spike generation can be reconciled with limited energy supply is presently unclear. To address this question, we performed direct recordings from the PV+-BC axon, the subcellular structure where active conductances for AP initiation and propagation are located. Surprisingly, the energy required for the AP was, on average, only ∼1.6 times the theoretical minimum. High energy efficiency emerged from the combination of fast inactivation of Na+ channels and delayed activation of Kv3-type K+ channels, which minimized ion flux overlap during APs. Thus, the complementary tuning of axonal Na+ and K+ channel gating optimizes both fast signaling properties and metabolic efficiency. Hu et al. demonstrate that action potentials in parvalbumin-expressing GABAergic interneuron axons are energetically efficient, which is highly unexpected given their brief duration. High energy efficiency emerges from the combination of fast inactivation of voltage-gated Na+ channels and delayed activation of Kv3 channels in the axon

NeRFtrinsic Four: An End-To-End Trainable NeRF Jointly Optimizing Diverse Intrinsic and Extrinsic Camera Parameters

Novel view synthesis using neural radiance fields (NeRF) is the state-of-the-art technique for generating high-quality images from novel viewpoints. Existing methods require a priori knowledge about extrinsic and intrinsic camera parameters. This limits their applicability to synthetic scenes, or real-world scenarios with the necessity of a preprocessing step. Current research on the joint optimization of camera parameters and NeRF focuses on refining noisy extrinsic camera parameters and often relies on the preprocessing of intrinsic camera parameters. Further approaches are limited to cover only one single camera intrinsic. To address these limitations, we propose a novel end-to-end trainable approach called NeRFtrinsic Four. We utilize Gaussian Fourier features to estimate extrinsic camera parameters and dynamically predict varying intrinsic camera parameters through the supervision of the projection error. Our approach outperforms existing joint optimization methods on LLFF and BLEFF. In addition to these existing datasets, we introduce a new dataset called iFF with varying intrinsic camera parameters. NeRFtrinsic Four is a step forward in joint optimization NeRF-based view synthesis and enables more realistic and flexible rendering in real-world scenarios with varying camera parameters