The diffusion of broadband telecommunications: the role of competition

This paper addresses the determinants of diffusion of broadband infrastructure by looking at the U.S. Federal States. It tries to identify in particular to what extent intra- and inter -platform competition contribute to accelerating the speed of diffusion. Panel data analysis results indicate that both types of competition significantly affect the rate of diffusion, although with different effect. Intra-platform competition seems to have a positive impact only initially on the rate of diffusion but then dissipates. For the longer term, inter -platform has a much more important role in driving the rate of diffusion. The study takes account of the impact of other variables measuring competition in the telecommunications sector as well.Broadband; Technological diffusion; Regulation and competition

Force monitor for training manual skills in the training of chiropractors

As part of their training, students of Chiropractic Medicine at Zürich are trained to acquire and then improve their manual and manipulative skills, especially their ability to deliver manipulative thrusts with a defined preloading force, an impulse that is delivered with an adequate and reproducible force within a defined time without letting up on the preload-pressure. In order to facilitate this process, objective feedback is paramount. This led to the idea of developing a force-measurement and -monitoring system. The newly developed system consists of a wireless device with a force sensor and an app that is running on standard smartphones. The device records the force applied to the sensor and transmits it via Bluetooth Low Energy (BLE) to the app. There it is visualised as a graph and can be evaluated. The system allows us to provide all students with a tool to develop their manual skills, and especially their thrusting technique. As the feedback given by the system can be record ed, progress can be monitored and students can be mentored accurately according to their strengths and weaknesses

Knowledge-Capital Meets New Economic Geography

We incorporate the now standard knowledge-capital model of multinational firms in a new economic geography setting. The theoretical predictions of our model suggest that unskilled labor mobility leads to less concentration of production than skilled labor mobility does. This is in line with empirical evidence that agglomeration of production among European nations is less pronounced than among US regions. Our model shows that the different patterns in labor mobility can explain actual differences in the spreading of industries. According to our welfare analysis, trade liberalization is likely Pareto-improving for a larger (smaller) country with mobile unskilled (skilled) labor. In the supplement, we investigate the sensitivity of our results in several respects. In the first section, we provide the figures of real factor rewards for the trade liberalization scenarios discussed in and underlying Figures 7 and 8 of the paper. Second, in Figures 3(n) - 5(v) (6(n) - 6b(v)) we infer the existence, or non-existence, of each firm type separately in the τ - λ L-space (τ - λ S-space) for country i firms and all four scenarios of firm regimes. Third, we illustrate how changes in the parameters μ, ρ and σ affect the outcome. Finally, we analyze how the asymmetric endowment with the immobile factor influences the core-periphery patterns.knowledge-capital model, new economic geography, unskilled labor mobility, skilled labor mobility

Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving

Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions. In order to develop robust sensors and algorithms, tests with current sensors in defined weather conditions are crucial for determining the impact of bad weather for each sensor. This work describes a testing and evaluation methodology that helps to benchmark novel sensor technologies and compare them to state-of-the-art sensors. As an example, gated imaging is compared to standard imaging under foggy conditions. It is shown that gated imaging outperforms state-of-the-art standard passive imaging due to time-synchronized active illumination

A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?

Autonomous driving at level five does not only means self-driving in the sunshine. Adverse weather is especially critical because fog, rain, and snow degrade the perception of the environment. In this work, current state of the art light detection and ranging (lidar) sensors are tested in controlled conditions in a fog chamber. We present current problems and disturbance patterns for four different state of the art lidar systems. Moreover, we investigate how tuning internal parameters can improve their performance in bad weather situations. This is of great importance because most state of the art detection algorithms are based on undisturbed lidar data

Retirement expectations, pension reforms and their impact on private wealth accumulation

We estimate the effect of pension reforms on households' expectations of retirement outcomes and private wealth accumulation decisions exploiting a decade of intense Italian pension reforms as a source of exogenous variation in expected pension wealth. The Survey of Household Income and Wealth, a large random sample of the Italian population, elicits expectations of the age at which workers expect to retire and of the ratio of pension benefits to pre-retirement income between 1989 and 2002. We find that workers have revised expectations in the direction suggested by the reform and that there is substantial offset between private wealth and perceived pension wealth, particularly by workers that are better informed about their pension wealth. Klassifikation: E21, H5

Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios

This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to a 50 megapixel camera with per-pixel depth available. Existing datasets, such as the KITTI benchmark, provide only sparse reference measurements with an order of magnitude lower angular resolution - these sparse measurements are treated as ground truth by existing depth estimation methods. We propose an evaluation methodology in four characteristic automotive scenarios recorded in varying weather conditions (day, night, fog, rain). As a result, our benchmark allows us to evaluate the robustness of depth sensing methods in adverse weather and different driving conditions. Using the proposed evaluation data, we demonstrate that current stereo approaches provide significantly more stable depth estimates than monocular methods and lidar completion in adverse weather. Data and code are available at https://github.com/gruberto/PixelAccurateDepthBenchmark.git.Comment: 3DV 201