Top Condensation without Fine-Tuning

Quadratic divergencies which lead to the usual fine-tuning or hierarchy problem are discussed in top condensation models. As in the Standard Model a cancellation of quadratic divergencies is not possible without the boson contributions in the radiative corrections which are absent in lowest order of an $1/N_c$-expansion. To deal with the cancellation of quadratic divergencies we propose therefore an expansion in the flavor degrees of freedom. In leading order we find the remarkable result that quadratic divergencies automatically disappear.Comment: LMU - 17/93, in LATEX, 12 pages and 3 pages of figures appended in Postscrip

Running Neutrino Mass Parameters in See-Saw Scenarios

We systematically analyze quantum corrections in see-saw scenarios, including effects from above the see-saw scales. We derive approximate renormalization group equations for neutrino masses, lepton mixings and CP phases, yielding an analytic understanding and a simple estimate of the size of the effects. Even for hierarchical masses, they often exceed the precision of future experiments. Furthermore, we provide a software package allowing for a convenient numerical renormalization group analysis, with heavy singlets being integrated out successively at their mass thresholds. We also discuss applications to model building and related topics.Comment: 49 pages, 9 figures; minor corrections in Sec. 6.5.1; the accompanying software packages REAP/MPT can be downloaded from http://www.ph.tum.de/~rg

Active Exploration for Inverse Reinforcement Learning

Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferring a reward function from expert demonstrations. Many IRL algorithms require a known transition model and sometimes even a known expert policy, or they at least require access to a generative model. However, these assumptions are too strong for many real-world applications, where the environment can be accessed only through sequential interaction. We propose a novel IRL algorithm: Active exploration for Inverse Reinforcement Learning (AceIRL), which actively explores an unknown environment and expert policy to quickly learn the expert's reward function and identify a good policy. AceIRL uses previous observations to construct confidence intervals that capture plausible reward functions and find exploration policies that focus on the most informative regions of the environment. AceIRL is the first approach to active IRL with sample-complexity bounds that does not require a generative model of the environment. AceIRL matches the sample complexity of active IRL with a generative model in the worst case. Additionally, we establish a problem-dependent bound that relates the sample complexity of AceIRL to the suboptimality gap of a given IRL problem. We empirically evaluate AceIRL in simulations and find that it significantly outperforms more naive exploration strategies.Comment: Presented at Conference on Neural Information Processing Systems (NeurIPS), 202

Proof-Producing Symbolic Execution for Binary Code Verification

We propose a proof-producing symbolic execution for verification of machine-level programs. The analysis is based on a set of core inference rules that are designed to give control over the tradeoff between preservation of precision and the introduction of overapproximation to make the application to real world code useful and tractable. We integrate our symbolic execution in a binary analysis platform that features a low-level intermediate language enabling the application of analyses to many different processor architectures. The overall framework is implemented in the theorem prover HOL4 to be able to obtain highly trustworthy verification results. We demonstrate our approach to establish sound execution time bounds for a control loop program implemented for an ARM Cortex-M0 processor

Hand Tracking based on Hierarchical Clustering of Range Data

Fast and robust hand segmentation and tracking is an essential basis for gesture recognition and thus an important component for contact-less human-computer interaction (HCI). Hand gesture recognition based on 2D video data has been intensively investigated. However, in practical scenarios purely intensity based approaches suffer from uncontrollable environmental conditions like cluttered background colors. In this paper we present a real-time hand segmentation and tracking algorithm using Time-of-Flight (ToF) range cameras and intensity data. The intensity and range information is fused into one pixel value, representing its combined intensity-depth homogeneity. The scene is hierarchically clustered using a GPU based parallel merging algorithm, allowing a robust identification of both hands even for inhomogeneous backgrounds. After the detection, both hands are tracked on the CPU. Our tracking algorithm can cope with the situation that one hand is temporarily covered by the other hand.Comment: Technical Repor

Connections between Dynamical and Renormalization Group Techniques in Top Condensation Models

Predictions for the ratio $M_W/m_t$ arise in top condensation models from different methods. One type of prediction stems from Pagels--Stokar relations based on the use of Ward Identities in the calculation of the \GB decay constants and expresses $M_W$ in terms of integrals containing the dynamically generated mass function $\Sigma_t(p^2)$. Another type of prediction emerges from the renormalization group equations via infrared quasi--fixed--points of the running top quark Yukawa coupling. We demonstrate in this paper that in the limit of a high cutoff these two methods lead to the same predictions for $M_W/m_t$ and $M_W/M_H$ in lowest loop order.Comment: Slightly revised version. LaTeX and uuencoded, packed Postscript figures. The complete paper, including figures, is also available via WWW at the following http URL http://www.physik.tu-muenchen.de/tumphy/d/T30d/PAPERS/TUM-HEP-216-95.ps.g