Discounting the Long-Distant Future: A Simple Explanation for the Weitzman-Gollier-Puzzle

In this paper, we reconsider the debate on Weitzman's (1998) suggestion to discount the long-run future at the lowest possible rate, referring to Gollier (2004) and Hepburn & Groom (2007). We show that, while Weitzman's use of the present value approach may indeed seem questionable, its outcome, i.e. a discount rate that is declining over time, is nevertheless reasonable, since it can be justified by assuming a plausible degree of risk aversion.discount rates, uncertainty, risk aversion

Discounting and Welfare Analysis Over Time: Choosing the ç

Based on the Ramsey equation and an ethically motivated rejection of pure utility time discount, the Stern Review on the Economics of Climate Change concentrates on the use of the elasticity of marginal utility ç in the intergenerational social welfare function. We support this position by showing that, also from the view point of sustainability, application of ç is preferable to the use of the pure time discount parameter ñ when a balanced distribution of utility across generations is to be brought about. After reviewing empirical studies on the size of ç we develop a novel axiomatic approach based on non–envy criteria by which we obtain values for ç lying in a range between 1 and 2. Whereas the starting point of the Stern Review quite explicitly is an ethical one, many critics of the Review deny this ethical stance and thus – as described in our paper – miss a crucial element of the Stern Review.Ramsey equation, discounting, sustainability, non-envy

Evaluation of single photon avalanche diode arrays for imaging fluorescence correlation spectroscopy : FPGA-based data readout and fast correlation analysis on CPUs, GPUs and FPGAs

The metabolism of all living organisms, and specifically also of their smallest constituents, the cell, is based on chemical reactions. A key factor determining the speed of these processes is transport of reactants, energy, and information within the and between the cells of an organism. It has been shown that the relevant transport processes also depend on the spatial organization of the cells. Such transport processes are typically investigated using fluorescence correlation spectroscopy (FCS) in combination with fluorescent labeling of the molecules of interest. In FCS, one observes the fluctuating fluorescence signal from a femtoliter-sized sub-volume within the sample (e.g. a cell). The variations in the intensity arise from the particles moving in and out of this sub-volume. By means of an autocorrelation analysis of the intensity signal, conclusion can be drawn regarding the concentration and the mobility parameters, such as the diffusion coefficient. Typically, one uses the laser focus of a confocal microscope for FCS measurements. But with this microscopy technique, FCS is limited to a single spot a every time. In order to conduct parallel multi-spot measurements, i.e. to create diffusion maps, FCS can be combined with the lightsheet based selective plane illumination microscopy (SPIM). This recent widefield microscopy technique allows observing a small plane of a sample (1-3um thick), which can be positioned arbitrarily. Usually, FCS on a SPIM is done using fast electron-multiplying charge-coupled device (EMCCD) cameras, which offer a limited temporal resolution (500us). Such a temporal resolution only allows measuring the motion of intermediately sized particles within a cell reliably. The limited temporal resolution renders the detection of even smaller molecules impossible. In this thesis, arrays of single photon avalanche diodes (SPADs) were used as detectors. Although SPAD-based image sensors still lack in sensitivity, they provide a significantly better temporal resolution (1-10us for full frames) that is not achievable with sensitive cameras and seem to be the ideal sensors for SPIM-FCS. In the course of this work, two recent SPAD arrays (developed in the groups of Prof. Edoardo Charbon, TU Delft, the Netherlands, and EPFL, Switzerland) were extensively characterized with regards to their suitability for SPIM-FCS. The evaluated SPAD arrays comprise 32x32 and 512x128 pixels and allow for frame rates of up to 300000 or 150000 frames per second, respectively. With these specifications, the latter array is one of the largest and fastest sensors that is currently available. During full-frame readout, it delivers a data rate of up to 1.2 GiB/s. For both arrays, suitable readout-hardware-based on field programmable gate arrays (FPGAs) was designed. To cope with the high data rate and to allow real-time correlation analysis, correlation algorithms were implemented and characterized on the three major high performance computing platforms, namely FPGAs, CPUs, and graphics processing units (GPUs). Of all three platforms, the GPU performed best in terms of correlation analysis, and a speed of 2.6 over real time was achieved for the larger SPAD array. Beside the lack in sensitivity, which could be accounted for by microlenses, a major drawback of the evaluated SPAD arrays was their afterpulsing. It appeared that the temporal structure superimposed the signal of the diffusion. Thus, extracting diffusion properties from the autocorrelation analysis only proved impossible. By additionally performing a spatial cross-correlation analysis such influences could be significantly minimized. Furthermore, this approach allowed for the determination of absolute diffusion coefficients without prior calibration. With that, spatially resolved measurements of fluorescent proteins in living cells could be conducted successfully

Graph-based Trajectory Prediction with Cooperative Information

For automated driving, predicting the future trajectories of other road users in complex traffic situations is a hard problem. Modern neural networks use the past trajectories of traffic participants as well as map data to gather hints about the possible driver intention and likely maneuvers. With increasing connectivity between cars and other traffic actors, cooperative information is another source of data that can be used as inputs for trajectory prediction algorithms. Connected actors might transmit their intended path or even complete planned trajectories to other actors, which simplifies the prediction problem due to the imposed constraints. In this work, we outline the benefits of using this source of data for trajectory prediction and propose a graph-based neural network architecture that can leverage this additional data. We show that the network performance increases substantially if cooperative data is present. Also, our proposed training scheme improves the network's performance even for cases where no cooperative information is available. We also show that the network can deal with inaccurate cooperative data, which allows it to be used in real automated driving environments.Comment: Accepted for publication at the 26th IEEE International Conference on Intelligent Transportation Systems 202

On the equivalence of two deformation schemes in quantum field theory

Two recent deformation schemes for quantum field theories on the two-dimensional Minkowski space, making use of deformed field operators and Longo-Witten endomorphisms, respectively, are shown to be equivalent.Comment: 14 pages, no figure. The final version is available under Open Access. CC-B

Identification of Threat Regions From a Dynamic Occupancy Grid Map for Situation-Aware Environment Perception

The advance towards higher levels of automation within the field of automated driving is accompanied by increasing requirements for the operational safety of vehicles. Induced by the limitation of computational resources, trade-offs between the computational complexity of algorithms and their potential to ensure safe operation of automated vehicles are often encountered. Situation-aware environment perception presents one promising example, where computational resources are distributed to regions within the perception area that are relevant for the task of the automated vehicle. While prior map knowledge is often leveraged to identify relevant regions, in this work, we present a lightweight identification of safety-relevant regions that relies solely on online information. We show that our approach enables safe vehicle operation in critical scenarios, while retaining the benefits of non-uniformly distributed resources within the environment perception.Comment: Accepted for publication at the 25th IEEE International Conference on Intelligent Transportation Systems 2022. V2: added IEEE copyright notice V3: Added DO

Environment Modeling Based on Generic Infrastructure Sensor Interfaces Using a Centralized Labeled-Multi-Bernoulli Filter

Urban intersections put high demands on fully automated vehicles, in particular, if occlusion occurs. In order to resolve such and support vehicles in unclear situations, a popular approach is the utilization of additional information from infrastructure-based sensing systems. However, a widespread use of such systems is circumvented by their complexity and thus, high costs. Within this paper, a generic interface is proposed, which enables a huge variety of sensors to be connected. The sensors are only required to measure very few features of the objects, if multiple distributed sensors with different viewing directions are available. Furthermore, a Labeled Multi-Bernoulli (LMB) filter is presented, which can not only handle such measurements, but also infers missing object information about the objects' extents. The approach is evaluated on simulations and demonstrated on a real-world infrastructure setup