808 research outputs found
Evaluation of A Resilience Embedded System Using Probabilistic Model-Checking
If a Micro Processor Unit (MPU) receives an external electric signal as
noise, the system function will freeze or malfunction easily. A new resilience
strategy is implemented in order to reset the MPU automatically and stop the
MPU from freezing or malfunctioning. The technique is useful for embedded
systems which work in non-human environments. However, evaluating resilience
strategies is difficult because their effectiveness depends on numerous,
complex, interacting factors.
In this paper, we use probabilistic model checking to evaluate the embedded
systems installed with the above mentioned new resilience strategy. Qualitative
evaluations are implemented with 6 PCTL formulas, and quantitative evaluations
use two kinds of evaluation. One is system failure reduction, and the other is
ADT (Average Down Time), the industry standard. Our work demonstrates the
benefits brought by the resilience strategy. Experimental results indicate that
our evaluation is cost-effective and reliable.Comment: In Proceedings ESSS 2014, arXiv:1405.055
Current-induced magnetization reversal in a (Ga,Mn)As-based magnetic tunnel junction
We report current-induced magnetization reversal in a ferromagnetic
semiconductor-based magnetic tunnel junction (Ga,Mn)As/AlAs/(Ga,Mn)As prepared
by molecular beam epitaxy on a p-GaAs(001) substrate. A change in
magneto-resistance that is asymmetric with respect to the current direction is
found with the excitation current of 10^6 A/cm^2. Contributions of both
unpolarized and spin-polarized components are examined, and we conclude that
the partial magnetization reversal occurs in the (Ga,Mn)As layer of smaller
magnetization with the spin-polarized tunneling current of 10^5 A/cm^2.Comment: 13 pages, 3 figure
Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach
Knowledge base completion (KBC) aims to predict missing information in a
knowledge base.In this paper, we address the out-of-knowledge-base (OOKB)
entity problem in KBC:how to answer queries concerning test entities not
observed at training time. Existing embedding-based KBC models assume that all
test entities are available at training time, making it unclear how to obtain
embeddings for new entities without costly retraining. To solve the OOKB entity
problem without retraining, we use graph neural networks (Graph-NNs) to compute
the embeddings of OOKB entities, exploiting the limited auxiliary knowledge
provided at test time.The experimental results show the effectiveness of our
proposed model in the OOKB setting.Additionally, in the standard KBC setting in
which OOKB entities are not involved, our model achieves state-of-the-art
performance on the WordNet dataset. The code and dataset are available at
https://github.com/takuo-h/GNN-for-OOKBComment: This paper has been accepted by IJCAI1
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