The Compressed Baryonic Matter Experiment at FAIR: Progress with feasibility studies and detector developments

The Compressed Baryonic Matter (CBM) experiment is being planned at the international research center FAIR, under realization next to the GSI laboratory in Darmstadt, Germany. Its physics programme addresses the QCD phase diagram in the region of highest net baryon densities. Of particular interest are the expected first order phase transition from partonic to hadronic matter, ending in a critical point, and modifications of hadron properties in the dense medium as a signal of chiral symmetry restoration. Laid out as a fixed-target experiment at the heavy-ion synchrotrons SIS-100/300, the detector will record both proton-nucleus and nucleus-nucleus collisions at beam energies between 10 and 45$A$ GeV. Hadronic, leptonic and photonic observables have to be measured with large acceptance. The interaction rates will reach 10 MHz to measure extremely rare probes like charm near threshold. Two versions of the experiment are being studied, optimized for either electron-hadron or muon identification, combined with silicon detector based charged-particle tracking and micro-vertex detection. The CBM physics requires the development of novel detector sytems, trigger and data acquisition concepts as well as innovative real-time reconstruction techniques. Progress with feasibility studies of the CBM experiment and the development of its detector systems are reported.Comment: 4 pages, 3 figures - FINAL - To appear in the conference proceedings for Quark Matter 2009, March 30 - April 4, Knoxville, Tennesse

OECD/CSNI ISP NR. 43 Rapid Boron Dilution Transient Tests For Code Verification Post Test Calculation With CFX-4

The need of the experimental support for validation of the computational tools to be applied to analyze the mixing of diluted slugs has been recognized in various countries. The test series for the International Standard Problem ISP-43 provides a platform for experiences to be applied to the simulation of a well-defined test series. Test A and B of the UM2x4 loop test facility were calculated with the CFD Code CFX-4.3. Sensitivity studies were made to analyze the used turbulence model and numerical errors. The results show good agreement with the experimental data for both tests

Analysis of the Copenhagen Accord pledges and its global climatic impacts‚ a snapshot of dissonant ambitions

This analysis of the Copenhagen Accord evaluates emission reduction pledges by individual countries against the Accord's climate-related objectives. Probabilistic estimates of the climatic consequences for a set of resulting multi-gas scenarios over the 21st century are calculated with a reduced complexity climate model, yielding global temperature increase and atmospheric CO2 and CO2-equivalent concentrations. Provisions for banked surplus emission allowances and credits from land use, land-use change and forestry are assessed and are shown to have the potential to lead to significant deterioration of the ambition levels implied by the pledges in 2020. This analysis demonstrates that the Copenhagen Accord and the pledges made under it represent a set of dissonant ambitions. The ambition level of the current pledges for 2020 and the lack of commonly agreed goals for 2050 place in peril the Accord's own ambition: to limit global warming to below 2 °C, and even more so for 1.5 °C, which is referenced in the Accord in association with potentially strengthening the long-term temperature goal in 2015. Due to the limited level of ambition by 2020, the ability to limit emissions afterwards to pathways consistent with either the 2 or 1.5 °C goal is likely to become less feasibl

π\pi-Electron Ferromagnetism in Metal Free Carbon Probed by Soft X-Ray Dichroism

Elemental carbon represents a fundamental building block of matter and the possibility of ferromagnetic order in carbon attracted widespread attention. However, the origin of magnetic order in such a light element is only poorly understood and has puzzled researchers. We present a spectromicroscopy study at room temperature of proton irradiated metal free carbon using the elemental and chemical specificity of x-ray magnetic circular dichroism (XMCD). We demonstrate that the magnetic order in the investigated system originates only from the carbon $\pi$-electron system.Comment: 10 pages 3 color figure

Induced Magnetic Ordering by Proton Irradiation in Graphite

We provide evidence that proton irradiation of energy 2.25 MeV on highly-oriented pyrolytic graphite samples triggers ferro- or ferrimagnetism. Measurements performed with a superconducting quantum interferometer device (SQUID) and magnetic force microscopy (MFM) reveal that the magnetic ordering is stable at room temperature.Comment: 3 Figure

NoiseGrad: enhancing explanations by introducing stochasticity to model weights

Many efforts have been made for revealing the decision-making process of black-box learning machines such as deep neural networks, resulting in useful local and global explanation methods. For local explanation, stochasticity is known to help: a simple method, called SmoothGrad, has improved the visual quality of gradient-based attribution by adding noise in the input space and taking the average over the noise. In this paper, we extend this idea and propose NoiseGrad that enhances both local and global explanation methods. Specifically, NoiseGrad introduces stochasticity in the weight parameter space, such that the decision boundary is perturbed. NoiseGrad is expected to enhance the local explanation, similarly to SmoothGrad, due to the dual relationship between the input perturbation and the decision boundary perturbation. Furthermore, NoiseGrad can be used to enhance global explanations. We evaluate NoiseGrad and its fusion with SmoothGrad -- FusionGrad -- qualitatively and quantitatively with several evaluation criteria, and show that our novel approach significantly outperforms the baseline methods. Both NoiseGrad and FusionGrad are method-agnostic and as handy as SmoothGrad using simple heuristics for the choice of hyperparameter setting without the need of fine-tuning.Comment: 19 pages, 16 figure

Visualizing the Diversity of Representations Learned by Bayesian Neural Networks

Explainable Artificial Intelligence (XAI) aims to make learning machines less opaque, and offers researchers and practitioners various tools to reveal the decision-making strategies of neural networks. In this work, we investigate how XAI methods can be used for exploring and visualizing the diversity of feature representations learned by Bayesian Neural Networks (BNNs). Our goal is to provide a global understanding of BNNs by making their decision-making strategies a) visible and tangible through feature visualizations and b) quantitatively measurable with a distance measure learned by contrastive learning. Our work provides new insights into the \emph{posterior} distribution in terms of human-understandable feature information with regard to the underlying decision making strategies. The main findings of our work are the following: 1) global XAI methods can be applied to explain the diversity of decision-making strategies of BNN instances, 2) Monte Carlo dropout with commonly used Dropout rates exhibit increased diversity in feature representations compared to the multimodal posterior approximation of MultiSWAG, 3) the diversity of learned feature representations highly correlates with the uncertainty estimate for the output and 4) the inter-mode diversity of the multimodal posterior decreases as the network width increases, while the intra mode diversity increases. These findings are consistent with the recent Deep Neural Networks theory, providing additional intuitions about what the theory implies in terms of humanly understandable concepts.Comment: 16 pages, 18 figure

Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors

Autonomous flying robots, e.g. multirotors, often rely on a neural network that makes predictions based on a camera image. These deep learning (DL) models can compute surprising results if applied to input images outside the training domain. Adversarial attacks exploit this fault, for example, by computing small images, so-called adversarial patches, that can be placed in the environment to manipulate the neural network's prediction. We introduce flying adversarial patches, where an image is mounted on another flying robot and therefore can be placed anywhere in the field of view of a victim multirotor. For an effective attack, we compare three methods that simultaneously optimize the adversarial patch and its position in the input image. We perform an empirical validation on a publicly available DL model and dataset for autonomous multirotors. Ultimately, our attacking multirotor would be able to gain full control over the motions of the victim multirotor.Comment: 6 pages, 5 figures, Workshop on Multi-Robot Learning, International Conference on Robotics and Automation (ICRA