Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon

How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems. Though previous work along this research line has shown some promising results, most existing methods either fail to significantly compress a well-trained deep network or require a heavy retraining process for the pruned deep network to re-boost its prediction performance. In this paper, we propose a new layer-wise pruning method for deep neural networks. In our proposed method, parameters of each individual layer are pruned independently based on second order derivatives of a layer-wise error function with respect to the corresponding parameters. We prove that the final prediction performance drop after pruning is bounded by a linear combination of the reconstructed errors caused at each layer. Therefore, there is a guarantee that one only needs to perform a light retraining process on the pruned network to resume its original prediction performance. We conduct extensive experiments on benchmark datasets to demonstrate the effectiveness of our pruning method compared with several state-of-the-art baseline methods

Analysis of Eco-Hydrological Characteristics of the Four Famous Carps' Spawning Grounds in the Middle Reach of Yangtze River

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Generating Semantic Adversarial Examples via Feature Manipulation

The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks). Traditional attacks perform unstructured pixel-wise perturbation to fool the classifier. An alternative approach is to have perturbations in the latent space. However, such perturbations are hard to control due to the lack of interpretability and disentanglement. In this paper, we propose a more practical adversarial attack by designing structured perturbation with semantic meanings. Our proposed technique manipulates the semantic attributes of images via the disentangled latent codes. The intuition behind our technique is that images in similar domains have some commonly shared but theme-independent semantic attributes, e.g. thickness of lines in handwritten digits, that can be bidirectionally mapped to disentangled latent codes. We generate adversarial perturbation by manipulating a single or a combination of these latent codes and propose two unsupervised semantic manipulation approaches: vector-based disentangled representation and feature map-based disentangled representation, in terms of the complexity of the latent codes and smoothness of the reconstructed images. We conduct extensive experimental evaluations on real-world image data to demonstrate the power of our attacks for black-box classifiers. We further demonstrate the existence of a universal, image-agnostic semantic adversarial example.Comment: arXiv admin note: substantial text overlap with arXiv:1705.09064 by other author

Pyrimidine catabolism is required to prevent the accumulation of 5-methyluridine in RNA

5-Methylated cytosine is a frequent modification in eukaryotic RNA and DNA influencing mRNA stability and gene expression. Here we show that free 5-methylcytidine (5mC) and 5-methyl-2′-deoxycytidine are generated from nucleic acid turnover in Arabidopsis thaliana, and elucidate how these cytidines are degraded, which is unclear in eukaryotes. First CYTIDINE DEAMINASE produces 5-methyluridine (5mU) and thymidine which are subsequently hydrolyzed by NUCLEOSIDE HYDROLASE 1 (NSH1) to thymine and ribose or deoxyribose. Interestingly, far more thymine is generated from RNA than from DNA turnover, and most 5mU is directly released from RNA without a 5mC intermediate, since 5-methylated uridine (m5U) is an abundant RNA modification (m5U/U ∼1%) in Arabidopsis. We show that m5U is introduced mainly by tRNA-SPECIFIC METHYLTRANSFERASE 2A and 2B. Genetic disruption of 5mU degradation in the NSH1 mutant causes m5U to occur in mRNA and results in reduced seedling growth, which is aggravated by external 5mU supplementation, also leading to more m5U in all RNA species. Given the similarities between pyrimidine catabolism in plants, mammals and other eukaryotes, we hypothesize that the removal of 5mU is an important function of pyrimidine degradation in many organisms, which in plants serves to protect RNA from stochastic m5U modification

Strain hardening of as-extruded Mg-xZn (x = 1, 2, 3 and 4 wt%) alloys

The influence of Zn on the strain hardening of as-extruded Mg-xZn (x = 1, 2, 3 and 4 wt%) magnesium alloys was investigated using uniaxial tensile tests at 10 s at room temperature. The strain hardening rate, the strain hardening exponent and the hardening capacity were obtained from true plastic stress-strain curves. There were almost no second phases in the as-extruded Mg-Zn magnesium alloys. Average grain sizes of the four as-extruded alloys were about 17.8 μm. With increasing Zn content from 1 to 4 wt%, the strain hardening rate increased from 2850 MPa to 6810 MPa at (σ-σ) = 60 MPa, the strain hardening exponent n increased from 0.160 to 0.203, and the hardening capacity, Hc increased from 1.17 to 2.34. The difference in strain hardening response of these Mg-Zn alloys might be mainly caused by weaker basal texture and more solute atoms in the α-Mg matrix with higher Zn content

Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features

PurposeCognitive impairment is generally found in individuals with type 2 diabetes mellitus (T2DM). Although they may not have visible symptoms of cognitive impairment in the early stages of the disorder, they are considered to be at high risk. Therefore, the classification of these patients is important for preventing the progression of cognitive impairment.MethodsIn this study, a convolutional neural network was used to construct a model for classifying 107 T2DM patients with and without cognitive impairment based on T1-weighted structural MRI. The Montreal cognitive assessment score served as an index of the cognitive status of the patients.ResultsThe classifier could identify T2DM-related cognitive decline with a classification accuracy of 84.85% and achieved an area under the curve of 92.65%.ConclusionsThe model can help clinicians analyze and predict cognitive impairment in patients and enable early treatment