Multiplicities in ultrarelativistic proton-(anti)proton collisions and negative binomial distribution fits

Likelihood ratio tests are performed for the hypothesis that charged-particle multiplicities measured in proton-(anti)proton collisions at $\sqrt{s}$ = 0.9 and 2.36 TeV are distributed according to the negative binomial form. Results indicate that the hypothesis should be rejected in the all cases of ALICE-LHC measurements in the limited pseudo-rapidity windows, whereas should be accepted in the corresponding cases of UA5 data. Possible explanations of that and of the disagreement with the least-squares fitting method are given.Comment: 14 pages, clarified version, reference added. To appear in International Journal of Modern Physics

Decision-Making in Autonomous Driving using Reinforcement Learning

The main topic of this thesis is tactical decision-making for autonomous driving. An autonomous vehicle must be able to handle a diverse set of environments and traffic situations, which makes it hard to manually specify a suitable behavior for every possible scenario. Therefore, learning-based strategies are considered in this thesis, which introduces different approaches based on reinforcement learning (RL). A general decision-making agent, derived from the Deep Q-Network (DQN) algorithm, is proposed. With few modifications, this method can be applied to different driving environments, which is demonstrated for various simulated highway and intersection scenarios. A more sample efficient agent can be obtained by incorporating more domain knowledge, which is explored by combining planning and learning in the form of Monte Carlo tree search and RL. In different highway scenarios, the combined method outperforms using either a planning or a learning-based strategy separately, while requiring an order of magnitude fewer training samples than the DQN method. A drawback of many learning-based approaches is that they create black-box solutions, which do not indicate the confidence of the agent\u27s decisions. Therefore, the Ensemble Quantile Networks (EQN) method is introduced, which combines distributional RL with an ensemble approach, to provide an estimate of both the aleatoric and the epistemic uncertainty of each decision. The results show that the EQN method can balance risk and time efficiency in different occluded intersection scenarios, while also identifying situations that the agent has not been trained for. Thereby, the agent can avoid making unfounded, potentially dangerous, decisions outside of the training distribution. Finally, this thesis introduces a neural network architecture that is invariant to permutations of the order in which surrounding vehicles are listed. This architecture improves the sample efficiency of the agent by the factorial of the number of surrounding vehicles

Improved Efficiency of Multilevel Monte Carlo for Stochastic PDE through Strong Pairwise Coupling

Multilevel Monte Carlo (MLMC) has become an important methodology in applied mathematics for reducing the computational cost of weak approximations. For many problems, it is well-known that strong pairwise coupling of numerical solutions in the multilevel hierarchy is needed to obtain efficiency gains. In this work, we show that strong pairwise coupling indeed is also important when (MLMC) is applied to stochastic partial differential equations (SPDE) of reaction-diffusion type, as it can improve the rate of convergence and thus improve tractability. For the (MLMC) method with strong pairwise coupling that was developed and studied numerically on filtering problems in [{\it Chernov et al., Numer. Math., 147 (2021), 71-125}], we prove that the rate of computational efficiency is higher than for existing methods. We also provide numerical comparisons with alternative coupling ideas on linear and nonlinear SPDE to illustrate the importance of this feature.Comment: 20 pages, 12 figure

Tactical decision-making for autonomous driving: A reinforcement learning approach

The tactical decision-making task of an autonomous vehicle is challenging, due to the diversity of the environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road users. This thesis introduces and compares three general approaches, based on reinforcement learning, to creating a tactical decision-making agent. The first method uses a genetic algorithm to automatically generate a rule based decision-making agent, whereas the second method is based on a Deep Q-Network agent. The third method combines the concepts of planning and learning, in the form of Monte Carlo tree search and deep reinforcement learning. The three approaches are applied to several highway driving cases in a simulated environment and outperform a commonly used baseline model by taking decisions that allow the vehicle to navigate 5% to 10% faster through dense traffic. However, the main advantage of the methods is their generality, which is indicated by applying them to conceptually different driving cases. Furthermore, this thesis introduces a novel way of applying a convolutional neural network architecture to a high level state description of interchangeable objects, which speeds up the learning process and eliminates all collisions in the test cases

Information filtering via Iterative Refinement

With the explosive growth of accessible information, expecially on the Internet, evaluation-based filtering has become a crucial task. Various systems have been devised aiming to sort through large volumes of information and select what is likely to be more relevant. In this letter we analyse a new ranking method, where the reputation of information providers is determined self-consistently.Comment: 10 pages, 3 figures. Accepted for publication on Europhysics Letter

Context and consequences of liquor sachets use among young people in Malawi

This article presents a relatively new phenomenon regarding alcohol related problems in Malawi; the context and consequences of the consumption of liquor sachets among young people. The results presented are part of a larger study looking at the prevalence and social norms related to alcohol use, as well as people’s opinions on policies and interventions related to alcohol in Malawi. The results presented here are from a qualitative component in three Malawian communities. The results imply that the introduction of sachets has contributed to an increase in alcohol consumption among young people. Major issues of concern are issues of age limits, packaging and alcohol content, as well as lack of empirical evidence on which to base policies and interventions. Finally, there is a need to mobilize positive adult role models for young people with regards to alcohol.Key words: Liquor sachets, Malawi, youth, alcohol consequences, qualitative stud

The sl n foam 2-category: A combinatorial formulation of Khovanov–Rozansky homology via categorical skew Howe duality

Abstract We give an elementary construction of colored sln link homology. The invariant takes values in a 2-category where 2-morphisms are given by foams, singular cobordisms between sln webs; applying a (TQFT-like) representable functor recovers (colored) Khovanov–Rozansky homology. Novel features of the theory include the introduction of “enhanced” foam facets which fix sign issues associated with the original matrix factorization formulation and the use of skew Howe duality to show that (enhanced) closed foams can be evaluated in a completely combinatorial manner. The latter answers a question posed in [42]