The effect of subsidies on R&D investment and success: do subsidy history and size matter?

This study provides insights into the effects of public R&D grants on R&D input and output of firms from Germany. Previous research has shown that the allocation of R&D project grants is rather stable regarding the pool of beneficiaries. The question is whether this participation pattern can be justified by its realized effects. In addition, the impact of the grant size on the effects is investigated. Therefore, I allow to a certain extent for heterogeneous treatment effects in these two dimensions. Using a sample of about 8,500 observations, a non-parametric matching approach with multiple treatments is applied to estimate the effects of public R&D grants on firm's R&D input. The results show that particularly frequently given grants as well as medium and large grants are suitable to increase the scope of firm-financed R&D plans. For the analysis of the effects on firm's R&D output the R&D expenditures are disentangled in R&D which would have been spent in the absence of the grant and publicly induced R&D, including the grant and the effect on private R&D expenditures. Basically both types of R&D are equally productive in terms of innovative output. For the statement that a rather stable pattern of program participation leads to a lower effectiveness of the instrument no evidence has been found. --R&D,Public Subsidies,Innovative Performance,Germany

Decoherence and definite outcomes

This thesis has three aims: (1) to clarify in detail the relation between the decoherence mechanism and the problem of definite outcomes, (2) to dispel common misconceptions about the measurement problem in quantum mechanics, and (3) to present some recent alternative approaches in the quest for a satisfactory solution of the definite outcomes problem.Comment: 102 pages, in English. Thesis work for the "Laura Magistrale Interfacolt\`a in Logica, Filosofia e Storia della Scienza" at Universit\`a degli studi di Firenze. Supervisors: Prof. Roberto Casalbuoni and Prof. Elena Castellan

The effect of subsidies on R&D investment and success : do subsidy history and size matter?

This study provides insights into the effects of public R&D grants on R&D input and output of firms from Germany. Previous research has shown that the allocation of R&D project grants is rather stable regarding the pool of beneficiaries. The question is whether this participation pattern can be justified by its realized effects. In addition, the impact of the grant size on the effects is investigated. Therefore, I allow to a certain extent for heterogeneous treatment effects in these two dimensions. Using a sample of about 8,500 observations, a non-parametric matching approach with multiple treatments is applied to estimate the effects of public R&D grants on firm's R&D input. The results show that particularly frequently given grants as well as medium and large grants are suitable to increase the scope of firm-financed R&D plans. For the analysis of the effects on firm's R&D output the R&D expenditures are disentangled in R&D which would have been spent in the absence of the grant and publicly induced R&D, including the grant and the effect on private R&D expenditures. Basically both types of R&D are equally productive in terms of innovative output. For the statement that a rather stable pattern of program participation leads to a lower effectiveness of the instrument no evidence has been found

Technology Mapping, Design for Testability, and Circuit Optimizations for NULL Convention Logic Based Architectures

Delay-insensitive asynchronous circuits have been the target of a renewed research effort because of the advantages they offer over traditional synchronous circuits. Minimal timing analysis, inherent robustness against power-supply, temperature, and process variations, reduced energy consumption, less noise and EMI emission, and easy design reuse are some of the benefits of these circuits. NULL Convention Logic (NCL) is one of the mainstream asynchronous logic design paradigms that has been shown to be a promising method for designing delay-insensitive asynchronous circuits. This dissertation investigates new areas in NCL design and test and is made of three sections. The first section discusses different CMOS implementations of NCL gates and proposes new circuit techniques to enhance their operation. The second section focuses on mapping multi-rail logic expressions to a standard NCL gate library, which is a form of technology mapping for a category of NCL design automation flows. Finally, the last section proposes design for testability techniques for a recently developed low-power variant of NCL called Sleep Convention Logic (SCL)

Carrier multipath mitigation in linear combinations of Global Navigation Satellite Systems measurements

Global Navigation Satellite Systems (GNSS) are the main systems that provide positioning, navigation and timing at a global level. They are being used in numerous applications in different sectors including transport, military, oil & gas, agriculture as well as location based services. A significant number of these applications require centimetre-level positioning accuracy, a challenging feat due to the many error sources that affect GNSS measurements. These include errors at the satellite, propagation medium, and receiver levels. Most of these errors can be mitigated by modeling, or by exploiting their spatial and temporal correlation characteristics. However, multipath errors, which result from the combination of the direct signal with reflected signals in the vicinity of the receiver antenna, are difficult to model and therefore, difficult to mitigate. Furthermore, high accuracy positioning applications typically rely on linear combinations of measurements at different frequencies (e.g. L1 and L2 in the case of the Global Positioning System) to mitigate frequency-dependent errors such as ionospheric errors (i.e. ionosphere free combination) or otherwise facilitate position calculation (e.g. Wide Lane observable). The multipath errors associated with such combinations are significantly larger than those of individual signals. The dependency of the multipath error on the environment and its low level in single frequency measurements (i.e. up to quarter of wavelength) makes modelling and mitigating it very difficult. Current techniques attempt to mitigate multipath errors for measurements at each individual frequency, independently of the error at other frequencies, even when linear combinations of measurements are used. The literature review carried out in this thesis has drawn three main conclusions regarding carrier multipath mitigation. Firstly, existing carrier multipath mitigation techniques are inaccurate, impractical or not effective. Secondly most of the practical techniques attempt to mitigate the error by de-weighting the measurements which are most prone to the multipath error (i.e measurement at low elevation). Thirdly, existing weighting techniques are oversimplified and do not reflect the error level accurately. In this research and for the first time, carrier multipath errors have been studied directly at the linear combination level. This is by exploiting the dispersive nature of multipath errors in order to model and correct them. New carrier multipath mitigation techniques applicable to linear combinations of measurements have been developed in this thesis on the basis of a new error model and a new observable referred to as the IFM (Inter-Frequency carrier Multipath). The IFM is computed from carrier phase measurements at two different frequencies, and corresponds to the combined multipath errors of those signals. In addition to multipath mitigation, this observable has various other applications. The well-defined relationship between the IFM and carrier multipath errors is used in this thesis to develop multipath mitigation techniques based on two approaches: multipath correction and measurement weighting. The new mitigation techniques are applicable to linear combinations of observations such as Wide Lane (WL) and Ionosphere Free (IF) carrier phase measurements in double differenced mode. The new multipath mitigation techniques have been validated using real data and the results compared with those obtained using the elevation weighting technique. The results show that the new methods developed in this thesis improve the mean error of horizontal position by up to 33% when using the IF combination. The results also show improvements of up to 78% in the time it takes to resolve ambiguities when using the WL combination.Open Acces

Quadrotor Accelerometer and Gyroscope Sensor Fault Diagnosis Using Nonlinear Adaptive Estimation Methods

This paper presents the design, analysis, and real-time experimental evaluation results of a nonlinear sensor fault diagnosis scheme for quadrotor unmanned air vehicles (UAV). The objective is to detect, isolate, and estimate sensor bias faults in accelerometer and gyroscope measurements. Based on the quadrotor dynamics and sensor models under consideration, the effects of sensor faults are represented as virtual actuator faults in the quadrotor state equation. Two nonlinear diagnostic estimators are designed to provide structured residuals for fault detection and isolation. Additionally, after the fault is detected and isolated, a nonlinear adaptive estimation scheme is employed for estimating the unknown fault magnitude. The proposed fault diagnosis scheme is capable of handling simultaneous faults in the accelerometer and gyroscope measurements. The effectiveness of the fault diagnosis method is demonstrated using an indoor real-time quadrotor UAV test environment

Decoherence and definite outcomes

This thesis has three aims: (1) to clarify in detail the relation between the decoherence mechanism and the problem of definite outcomes, (2) to dispel common misconceptions about the measurement problem in quantum mechanics, and (3) to present some recent alternative approaches in the quest for a satisfactory solution of the definite outcomes problem

Demystifying reinforcement learning approaches for production scheduling

Recent years has seen a sharp rise in interest pertaining to Reinforcement Learning (RL) approaches for production scheduling. This is because RL is seen as a an advantageous compromise between the two most typical scheduling solution approaches, namely priority rules and exact approaches. However, there are many variations of both production scheduling problems and RL solutions. Additionally, the RL production scheduling literature is characterized by a lack of standardization, which leads to the field being shrouded in mysticism. The burden of showcasing the exact situations where RL outshines other approaches still lies with the research community. To pave the way towards this goal, we make the following four contributions to the scientific community, aiding in the process of RL demystification. First, we develop a standardization framework for RL scheduling approaches using a comprehensive literature review as a conduit. Secondly, we design and implement FabricatioRL, an open-source benchmarking simulation framework for production scheduling covering a vast array of scheduling problems and ensuring experiment reproducibility. Thirdly, we create a set of baseline scheduling algorithms sharing some of the RL advantages. The set of RL-competitive algorithms consists of a Constraint Programming (CP) meta-heuristic developed by us, CP3, and two simulation-based approaches namely a novel approach we call Simulation Search and Monte Carlo Tree Search. Fourth and finally, we use FabricatioRL to build two benchmarking instances for two popular stochastic production scheduling problems, and run fully reproducible experiments on them, pitting Double Deep Q Networks (DDQN) and AlphaGo Zero (AZ) against the chosen baselines and priority rules. Our results show that AZ manages to marginally outperform priority rules and DDQN, but fails to outperform our competitive baselines