1,173 research outputs found
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 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
-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 -electron system.Comment: 10 pages 3 color figure
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
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
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