Different approaches to optimize high-definition matrix headlights to improve computer vision

The contribution at hand presents and compares different online optimization approaches of dynamic illumination of matrix headlamps to improve automatic object recognition by neural networks. The approaches optimize, on the one hand, the network confidence and, on the other hand, the brightness of the image, the Weber contrast, and the gradient distribution on the image depending on the headlight beam pattern. The evaluation shows no objectively seen best cost function for the scenario studied, and selecting a cost function is a subjective decision. Optimizing the beam pattern to increase the confidence and intersection over union leads to inhomogeneous and subjectively disturbing beam patterns, and using contrast and gradient leads to similar results

Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess

In this work, we adapt a training approach inspired by the original AlphaGo system to play the imperfect information game of Reconnaissance Blind Chess. Using only the observations instead of a full description of the game state, we first train a supervised agent on publicly available game records. Next, we increase the performance of the agent through self-play with the on-policy reinforcement learning algorithm Proximal Policy Optimization. We do not use any search to avoid problems caused by the partial observability of game states and only use the policy network to generate moves when playing. With this approach, we achieve an ELO of 1330 on the RBC leaderboard, which places our agent at position 27 at the time of this writing. We see that self-play significantly improves performance and that the agent plays acceptably well without search and without making assumptions about the true game state.Comment: 4 Pages, IEEE Conference on Games 2022 short pape

Emerging population of gap-opening planets around type-A stars -- Long-term evolution of the forming planets around HD 163296

The presence of forming planets embedded in their protoplanetary disks has been inferred from the detection of multiring structures in such disks. Most of these suspected planets are undetectable by direct imaging observations at current measurement sensitivities. Inward migration and accretion might make these putative planets accessible to the Doppler method, but the actual extent of growth and orbital evolution remains unconstrained. Under the premise that the gaps in the disk around HD 163296 originate from new-born planets, we investigate if and under which circumstances the gap-opening planets could represent progenitors of the exoplanet population detected around A-type stars. In particular, we study the dependence of final planetary masses and orbital parameters on the viscosity of the disk. The evolution of the embedded planets was simulated throughout the disk lifetime and up to 100 Myr after the dispersal of the disk, taking the evolving disk structure and a likely range of disk lifetimes into account. We find that the final configuration of the planets is largely determined by the $\alpha$ viscosity parameter of the disk and less dependent on the choice for the disk lifetime and the initial planetary parameters. If we assume that planets such as those in HD 163296 evolve to form the observed exoplanet population of A-type stars, a $\alpha$ parameter on the order of $3.16 \times 10^{-4} \lesssim \alpha \lesssim 10^{-3}$ is required for the disks to induce sufficiently high migration rates. Depending on whether or not future direct imaging surveys will uncover a larger number of planets with $m_\mathrm{pl} \lesssim 3 M_\mathrm{Jup}$ and $a_\mathrm{pl} \gtrsim 10 \mathrm{AU}$ we expect the $\alpha$ parameter to be at the lower or upper end of this range, always under the assumption that such disks indeed harbor wide orbit planets.Comment: Accepted for publication in Astronomy and Astrophysics, 13 pages, 7 figure

Evidence for the late MMN as a neurophysiological endophenotype for dyslexia.

Dyslexia affects 5-10% of school-aged children and is therefore one of the most common learning disorders. Research on auditory event related potentials (AERP), particularly the mismatch negativity (MMN) component, has revealed anomalies in individuals with dyslexia to speech stimuli. Furthermore, candidate genes for this disorder were found through molecular genetic studies. A current challenge for dyslexia research is to understand the interaction between molecular genetics and brain function, and to promote the identification of relevant endophenotypes for dyslexia. The present study examines MMN, a neurophysiological correlate of speech perception, and its potential as an endophenotype for dyslexia in three groups of children. The first group of children was clinically diagnosed with dyslexia, whereas the second group of children was comprised of their siblings who had average reading and spelling skills and were therefore "unaffected" despite having a genetic risk for dyslexia. The third group consisted of control children who were not related to the other groups and were also unaffected. In total, 225 children were included in the study. All children showed clear MMN activity to/da/-/ba/contrasts that could be separated into three distinct MMN components. Whilst the first two MMN components did not differentiate the groups, the late MMN component (300-700 ms) revealed significant group differences. The mean area of the late MMN was attenuated in both the dyslexic children and their unaffected siblings in comparison to the control children. This finding is indicative of analogous alterations of neurophysiological processes in children with dyslexia and those with a genetic risk for dyslexia, without a manifestation of the disorder. The present results therefore further suggest that the late MMN might be a potential endophenotype for dyslexia

Usage of material properties of 3D objects for an improved illumination by high-definition matrix headlights

The contribution at hand presents a novel algorithm to improve visual perception for humans and machine vision algorithms by virtually adjusting the illumination of high-definition (HD) matrix headlights as a function of different material properties in the environment, such that each material can be illuminated with a different illumination intensity. Since the material properties affect the visual perception of objects, this approach allows the attention of human drivers and other human traffic participants to be focused on selected areas or significantly increase the detection quality for machine vision algorithms in the case of automated driving, while it is possible to save energy at the same time