On The Spatial Economics of Knowledge Accumulation

My thesis investigates the spatial economics of knowledge accumulation. The main contributions of my work are the following: First, I explore the theoretical foundations and the economic relevance of the spatial heterogeneity in knowledge accumulation. The distinction between the creation and transmission of knowledge and their respective local determinants are the main focus of this exploration that links endogenous growth theory and recent research on spatial aspects of human capital and innovation. Second, I present a theoretical analysis on the role of face-to-face interactions in knowledge spillovers. This search-theoretic model considers the creation and transmission of knowledge and determines that knowledge externalities do not reach their optimal extent because agents choose their partners for interaction too narrowly. Third, my empirical analysis for European regions shows that geographical and technological proximity foster innovative spillovers between regions. A spatial-autoregressive estimation of the reduced form of the knowledge production function provides the framework for this investigation

Full kinematic reconstruction of charged B mesons with the upgraded Inner Tracking System of the ALICE Experiment

In this thesis, the performance of the full kinematic reconstruction of B+ mesons in the decay channel B+ to D0bar Pi+ (D0bar to K+ Pi-) and charge conjugates for the 0-10% most central Pb–Pb collisions at sqrt(sNN) = 5.5 TeV is demonstrated for the upgraded ALICE Experiment, which is planned before Run 3 of the Large Hadron Collider (LHC), beginning in 2020. Within the scope of the foreseen detector and readout upgrades to inspect all Pb–Pb collisions at their interaction rate of 50 kHz, in particular through the installation of a new high-granularity pixel inner tracker, for the first time these rare signals will become accessible using full kinematic reconstruction in central Pb–Pb collisions in ALICE at mid-rapidity at the LHC. Topological and kinematic criteria are used to select the beauty signal against the large combinatorial and correlated background. In addition to available full Monte Carlo (MC) simulations, a fast MC simulation, which includes parameterizations of all relevant detector effects, was developed and is now generally available for all rare probe studies with the upgraded ALICE detector. The fast simulation was used to improve the estimate on the residual combinatorial background in order to maximize the expected signalto- background ratio and statistical significance. Within the uncertainties of the expected signal yield, a significant measurement (>5) will be possible down to pT > 2.0 GeV/c, corresponding to about 88% of the yield. The signal-to-background ratio lies between 0.01 and 4.0, increasing with pT. The required reference statistics in p+p collisions at sqrt(s) = 5.5 TeV was estimated to be about 100 1/pb. Considering the calculated expected statistics, the precision of the measurements of the nuclear modification factor RAA and the elliptic flow v2 were estimated. A measurement of the theoretically predicted RAA of 0.2–0.5 above pT > 5.0 GeV/c will be possible, while the sensitivity for lower momenta on the enhancement above RAA = 1.0 is strongly model dependent. The separation power between the theoretically predicted v2 and a non-flow scenario is within 1.6–5.3 % for central and more peripheral collisions, depending on the actual magnitude and the available statistical precision

Continuous fusion of motion data using an axis-angle rotation representation with uniform B-spline

The fusion of motion data is key in the fields of robotic and automated driving. Most existing approaches are filter-based or pose-graph-based. By using filter-based approaches, parameters should be set very carefully and the motion data can usually only be fused in a time forward direction. Pose-graph-based approaches can fuse data in time forward and backward directions. However, pre-integration is needed by applying measurements from inertial measurement units. Additionally, both approaches only provide discrete fusion results. In this work, we address this problem and present a uniform B-spline-based continuous fusion approach, which can fuse motion measurements from an inertial measurement unit and pose data from other localization systems robustly, accurately and efficiently. In our continuous fusion approach, an axis-angle is applied as our rotation representation method and uniform B-spline as the back-end optimization base. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results, which again supports our continuous fusion concept

Semantic evidential grid mapping using monocular and stereo cameras

Accurately estimating the current state of local traffic scenes is one of the key problems in the development of software components for automated vehicles. In addition to details on free space and drivability, static and dynamic traffic participants and information on the semantics may also be included in the desired representation. Multi-layer grid maps allow the inclusion of all of this information in a common representation. However, most existing grid mapping approaches only process range sensor measurements such as Lidar and Radar and solely model occupancy without semantic states. In order to add sensor redundancy and diversity, it is desired to add vision-based sensor setups in a common grid map representation. In this work, we present a semantic evidential grid mapping pipeline, including estimates for eight semantic classes, that is designed for straightforward fusion with range sensor data. Unlike other publications, our representation explicitly models uncertainties in the evidential model. We present results of our grid mapping pipeline based on a monocular vision setup and a stereo vision setup. Our mapping results are accurate and dense mapping due to the incorporation of a disparity- or depth-based ground surface estimation in the inverse perspective mapping. We conclude this paper by providing a detailed quantitative evaluation based on real traffic scenarios in the KITTI odometry benchmark dataset and demonstrating the advantages compared to other semantic grid mapping approaches

Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning

Despite recent advances in reinforcement learning (RL), its application in safety critical domains like autonomous vehicles is still challenging. Although punishing RL agents for risky situations can help to learn safe policies, it may also lead to highly conservative behavior. In this paper, we propose a distributional RL framework in order to learn adaptive policies that can tune their level of conservativity at run-time based on the desired comfort and utility. Using a proactive safety verification approach, the proposed framework can guarantee that actions generated from RL are fail-safe according to the worst-case assumptions. Concurrently, the policy is encouraged to minimize safety interference and generate more comfortable behavior. We trained and evaluated the proposed approach and baseline policies using a high level simulator with a variety of randomized scenarios including several corner cases which rarely happen in reality but are very crucial. In light of our experiments, the behavior of policies learned using distributional RL can be adaptive at run-time and robust to the environment uncertainty. Quantitatively, the learned distributional RL agent drives in average 8 seconds faster than the normal DQN policy and requires 83\% less safety interference compared to the rule-based policy with slightly increasing the average crossing time. We also study sensitivity of the learned policy in environments with higher perception noise and show that our algorithm learns policies that can still drive reliable when the perception noise is two times higher than the training configuration for automated merging and crossing at occluded intersections

Correction of stratospheric age-of-air derived from SF 6 for the effect of chemical sinks

Observational monitoring of the stratospheric transport circulation, the Brewer-Dobson-Circulation (BDC), is crucial to estimate any decadal to long-term changes therein, a prerequisite to interpret trends in stratospheric composition and to constrain the consequential impacts on climate. The transport time along the BDC (i.e., the mean age of stratospheric air, AoA) can best be deduced from trace gas measurements of tracers which increase linearly in time and are chemically passive. The gas SF6 is often used to deduce AoA, because it has been increasing monotonically since the ~1950s, and previously its chemical sinks in the mesosphere have been assumed to be negligible for AoA estimates. However, recent studies have shown that the chemical sinks of SF6 are stronger than assumed, and become increasingly relevant with rising SF6 concentrations. To adjust biases in AoA that result from the chemical SF6 sinks, we here propose a simple correction scheme for SF6-based AoA estimates accounting for the time-dependent effects of chemical sinks. The correction scheme is based on theoretical considerations with idealized assumptions, resulting in a relation between ideal AoA and apparent AoA which is a function of the tropospheric reference time-series of SF6 and of the AoA-dependent effective lifetime of SF6. The correction method is thoroughly tested within a self-consistent data set from a climate model that includes explicit calculation of chemical SF6 sinks. It is shown within the model that the correction successfully reduces biases in SF6-based AoA to less than 5 % for mean ages below 5 years. Tests with using only sub-sampled data for deriving the fit coefficients show that applying the correction scheme even with imperfect knowledge of the sink is far superior to not applying a sink correction. Further, we show that based on currently available measurements, we are not able to constrain the fit parameters of the correction scheme based on observational data alone. However, the model-based correction curve lies within the observational uncertainty, and we thus recommend to use the model-derived fit coefficients until more high-quality measurements will be able to further constrain the correction scheme. The application of the correction scheme to AoA from satellites and in-situ data suggests that it is highly beneficial to reconcile different observational estimates of mean AoA

First detection of ammonia (NH₃) in the Asian summer monsoon upper troposphere

Ammonia (NH3) has been detected in the upper troposphere by the analysis of averaged MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) infrared limb-emission spectra. We have found enhanced amounts of NH3 within the region of the Asian summer monsoon at 12–15 km altitude. Three-monthly, 10° longitude  ×  10° latitude average profiles reaching maximum mixing ratios of around 30 pptv in this altitude range have been retrieved, with a vertical resolution of 3–8 km and estimated errors of about 5 pptv. These observations show that loss processes during transport from the boundary layer to the upper troposphere within the Asian monsoon do not deplete the air entirely of NH3. Thus, ammonia might contribute to the so-called Asian tropopause aerosol layer by the formation of ammonium aerosol particles. On a global scale, outside the monsoon area and during different seasons, we could not detect enhanced values of NH3 above the actual detection limit of about 3–5 pptv. This upper bound helps to constrain global model simulations

Differences in ozone retrieval in MIPAS channels A and AB: a spectroscopic issue

Discrepancies in ozone retrievals in MIPAS channels A (685–970cm−1) and AB (1020–1170cm−1) have been a long-standing problem in MIPAS data analysis, amounting to an interchannel bias (AB–A) of up to 8% between ozone volume mixing ratios in the altitude range 30–40km. We discuss various candidate explanations, among them forward model and retrieval algorithm errors, interchannel calibration inconsistencies and spectroscopic data inconsistencies. We show that forward-modelling errors as well as errors in the retrieval algorithm can be ruled out as an explanation because the bias can be reproduced with an entirely independent retrieval algorithm (GEOFIT), relying on a different forward radiative transfer model. Instrumental and calibration issues can also be refuted as an explanation because ozone retrievals based on balloon-borne measurements with a different instrument (MIPAS-B) and an independent level-1 data processing scheme produce a rather similar interchannel bias. Thus, spectroscopic inconsistencies in the MIPAS database used for ozone retrieval are practically the only reason left. To further investigate this issue, we performed retrievals using additional spectroscopic databases. Various versions of the HITRAN database generally produced rather similar channel AB–A differences. Use of a different database, namely GEISA-2015, led to similar results in channel AB, but to even higher ozone volume mixing ratios for channel A retrievals, i.e. to a reversal of the bias. We show that the differences in MIPAS channel A retrievals result from about 13% lower air-broadening coefficients of the strongest lines in the GEISA-2015 database. Since the errors in line intensity of the major lines used in MIPAS channels A and AB are reported to be considerably lower than the observed bias, we posit that a major part of the channel AB–A differences can be attributed to inconsistent air-broadening coefficients as well. To corroborate this assumption we show some clearly inconsistent air-broadening coefficients in the HITRAN-2008 database. The interchannel bias in retrieved ozone amounts can be reduced by increasing the air-broadening coefficients of the lines in MIPAS channel AB in the HITRAN-2008 database by 6%–8%