1,650 research outputs found
Spiking Neural P Systems with Addition/Subtraction Computing on Synapses
Spiking neural P systems (SN P systems, for short) are a class of distributed
and parallel computing models inspired from biological spiking neurons. In this paper,
we introduce a variant called SN P systems with addition/subtraction computing on
synapses (CSSN P systems). CSSN P systems are inspired and motivated by the shunting
inhibition of biological synapses, while incorporating ideas from dynamic graphs and
networks. We consider addition and subtraction operations on synapses, and prove that
CSSN P systems are computationally universal as number generators, under a normal
form (i.e. a simplifying set of restrictions)
Iterative Object and Part Transfer for Fine-Grained Recognition
The aim of fine-grained recognition is to identify sub-ordinate categories in
images like different species of birds. Existing works have confirmed that, in
order to capture the subtle differences across the categories, automatic
localization of objects and parts is critical. Most approaches for object and
part localization relied on the bottom-up pipeline, where thousands of region
proposals are generated and then filtered by pre-trained object/part models.
This is computationally expensive and not scalable once the number of
objects/parts becomes large. In this paper, we propose a nonparametric
data-driven method for object and part localization. Given an unlabeled test
image, our approach transfers annotations from a few similar images retrieved
in the training set. In particular, we propose an iterative transfer strategy
that gradually refine the predicted bounding boxes. Based on the located
objects and parts, deep convolutional features are extracted for recognition.
We evaluate our approach on the widely-used CUB200-2011 dataset and a new and
large dataset called Birdsnap. On both datasets, we achieve better results than
many state-of-the-art approaches, including a few using oracle (manually
annotated) bounding boxes in the test images.Comment: To appear in ICME 2017 as an oral pape
Field Experimental Study on Corrosion Mechanism of Well Lai 14-9
Casing corrosion is serious and the injection efficiency is considerably low due to high injecting water corrosion in Well Lai 14-9. Casing corrosion mechanism is retrieved through SEM observation, energy spectrum analysis, XRD analysis spectrum, TGA analysis of corrosion fouling. The results show casing corrosion mechanism varies with depths; casing corrosion is the result of the combined action of carbon dioxide, dissolved oxygen, sulfate-reducing bacteria and high salinity. To extend the life of casing, appropriate casing protection measures should be adopted, which can reduces the cost of production of the oilfield.Key words: Corrosion mechanism; Fouling; SEM observation; XRD analysis spectrum; TGA analysi
On the Robustness of Post-hoc GNN Explainers to Label Noise
Proposed as a solution to the inherent black-box limitations of graph neural
networks (GNNs), post-hoc GNN explainers aim to provide precise and insightful
explanations of the behaviours exhibited by trained GNNs. Despite their recent
notable advancements in academic and industrial contexts, the robustness of
post-hoc GNN explainers remains unexplored when confronted with label noise. To
bridge this gap, we conduct a systematic empirical investigation to evaluate
the efficacy of diverse post-hoc GNN explainers under varying degrees of label
noise. Our results reveal several key insights: Firstly, post-hoc GNN
explainers are susceptible to label perturbations. Secondly, even minor levels
of label noise, inconsequential to GNN performance, harm the quality of
generated explanations substantially. Lastly, we engage in a discourse
regarding the progressive recovery of explanation effectiveness with escalating
noise levels
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