372 research outputs found
The Relationships Between the Level of Lignin, a Secondary Metabolite in Soybean Plant, and Aphid Resistance in Soybeans
In the present report, the relationship was discussed between the level of lignin-one of the secondary metabolites in soybean plant and the chemical defense reaction of soybean to the soybean aphid (Aphis glycines Muts). Experimental results indicated that the cultivars with higher level of lignin are more resistant to the damage of aphids than those with lower level of lignin. Lignin is one of the compounds that are responsible to the chemical defense reaction of soybean. This finding laid a foundation for the elucidation of the mechanism of aphid resistance in plants and its biochemical basis.Originating text in Chinese.Citation: Hu, Qi, Zhao, Jianwei, Cui, Jianwen. (1993). The Relationships Between the Level of Lignin, a Secondary Metabolite in Soybean Plant, and Aphid Resistance in Soybeans. Plant Protection (Institute of Plant Protection, CAAS, China), 19(1), 8-9
Breaking through Deterministic Barriers: Randomized Pruning Mask Generation and Selection
It is widely acknowledged that large and sparse models have higher accuracy
than small and dense models under the same model size constraints. This
motivates us to train a large model and then remove its redundant neurons or
weights by pruning. Most existing works pruned the networks in a deterministic
way, the performance of which solely depends on a single pruning criterion and
thus lacks variety. Instead, in this paper, we propose a model pruning strategy
that first generates several pruning masks in a designed random way.
Subsequently, along with an effective mask-selection rule, the optimal mask is
chosen from the pool of mask candidates. To further enhance efficiency, we
introduce an early mask evaluation strategy, mitigating the overhead associated
with training multiple masks. Our extensive experiments demonstrate that this
approach achieves state-of-the-art performance across eight datasets from GLUE,
particularly excelling at high levels of sparsity
Towards Robust Pruning: An Adaptive Knowledge-Retention Pruning Strategy for Language Models
The pruning objective has recently extended beyond accuracy and sparsity to
robustness in language models. Despite this, existing methods struggle to
enhance robustness against adversarial attacks when continually increasing
model sparsity and require a retraining process. As humans step into the era of
large language models, these issues become increasingly prominent. This paper
proposes that the robustness of language models is proportional to the extent
of pre-trained knowledge they encompass. Accordingly, we introduce a
post-training pruning strategy designed to faithfully replicate the embedding
space and feature space of dense language models, aiming to conserve more
pre-trained knowledge during the pruning process. In this setup, each layer's
reconstruction error not only originates from itself but also includes
cumulative error from preceding layers, followed by an adaptive rectification.
Compared to other state-of-art baselines, our approach demonstrates a superior
balance between accuracy, sparsity, robustness, and pruning cost with BERT on
datasets SST2, IMDB, and AGNews, marking a significant stride towards robust
pruning in language models
A prediction model of specific productivity index using least square support vector machine method
In the design of oilfield development plans, specific productivity index plays a vital role. Especially for offshore oilfields, affected by development costs and time limits, there are shortcomings of shorter test time and fewer test sampling points. Therefore, it is very necessary to predict specific productivity index. In this study, a prediction model of the specific productivity index is established by combining the principle of least squares support vector machine (LS-SVM) with the calculation method of the specific productivity index. The model uses logging parameters, crude oil experimental parameters and the specific productivity index of a large number of test well samples as input and output items respectively, and finally predicts the specific productivity index of non-test wells. It reduces the errors caused by short training time, randomness of training results and insufficient learning. A large number of sample data from the Huanghekou Sag in Bohai Oilfield were used to verify the prediction model. Comparing the specific productivity index prediction results of LS-SVM and artificial neural networks (ANNs) with actual well data respectively, the LS-SVM model has a better fitting effect, with an error of only 3.2%, which is 12.1% lower than ANNs. This study can better reflect the impact of different factors on specific productivity index, and it has important guiding significance for the evaluation of offshore oilfield productivity.Cited as: Wu, C., Wang, S., Yuan, J., Li, C., Zhang, Q. A prediction model of specific productivity index using least square support vector machine method. Advances in Geo-Energy Research, 2020, 4(4): 460-467, doi: 10.46690/ager.2020.04.1
- …