Graph-Based Similarity of Neural Network Representations

Understanding the black-box representations in Deep Neural Networks (DNN) is an essential problem in deep learning. In this work, we propose Graph-Based Similarity (GBS) to measure the similarity of layer features. Contrary to previous works that compute the similarity directly on the feature maps, GBS measures the correlation based on the graph constructed with hidden layer outputs. By treating each input sample as a node and the corresponding layer output similarity as edges, we construct the graph of DNN representations for each layer. The similarity between graphs of layers identifies the correspondences between representations of models trained in different datasets and initializations. We demonstrate and prove the invariance property of GBS, including invariance to orthogonal transformation and invariance to isotropic scaling, and compare GBS with CKA. GBS shows state-of-the-art performance in reflecting the similarity and provides insights on explaining the adversarial sample behavior on the hidden layer space

ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs

A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal. However, in many real-world settings, the size of parallel annotated training data is restricted. Additionally, prior cross-lingual mapping research has mainly focused on the word level. This raises the question of whether such techniques can also be applied to effortlessly obtain cross-lingually aligned sentence representations. To this end, we propose an Adversarial Bi-directional Sentence Embedding Mapping (ABSent) framework, which learns mappings of cross-lingual sentence representations from limited quantities of parallel data

Understanding the Dynamics of DNNs Using Graph Modularity

There are good arguments to support the claim that deep neural networks (DNNs) capture better feature representations than the previous hand-crafted feature engineering, which leads to a significant performance improvement. In this paper, we move a tiny step towards understanding the dynamics of feature representations over layers. Specifically, we model the process of class separation of intermediate representations in pre-trained DNNs as the evolution of communities in dynamic graphs. Then, we introduce modularity, a generic metric in graph theory, to quantify the evolution of communities. In the preliminary experiment, we find that modularity roughly tends to increase as the layer goes deeper and the degradation and plateau arise when the model complexity is great relative to the dataset. Through an asymptotic analysis, we prove that modularity can be broadly used for different applications. For example, modularity provides new insights to quantify the difference between feature representations. More crucially, we demonstrate that the degradation and plateau in modularity curves represent redundant layers in DNNs and can be pruned with minimal impact on performance, which provides theoretical guidance for layer pruning. Our code is available at https://github.com/yaolu-zjut/Dynamic-Graphs-Construction.Comment: Accepted by ECCV 202

Experimental Study on the Effect of Air-Doors Control Adjacent to the Fire Source on the Characteristics of Smoke Back-Layering

Air-doors are important facilities for regulating the air flow in a mine ventilation network. It is of value to study the influence of air-doors, which are adjacent to a fire source on smoke back-layering in order to build a rational ventilation system. By regulating air-doors in a mine ventilation network test platform, two typical mine ventilation networks, with parallel branches and a diagonal branch, were constructed. During the study, into the closing degree of the air-doors adjacent to a fire source in a ventilation network with parallel branches, the back-layering length is up to 3.70 m when the ventilation velocity is 1.40 m/s. When the air-door on the return side of the adjacent branch is closed, the back-layering subsides within 1 min and the upstream temperature drops rapidly to normal. When the air-door is half closed, there is still a back-layering flow within 5 min. Smoke control, with the air-door is closed, is better than when the air-door is half closed. Based on this, tests into the influence of the closing position of air-doors, which are adjacent to a fire source, were carried out in a ventilation network with a diagonal branch. Results indicate that when the ventilation velocity is 1.70 m/s, the back-layering flow spreads to the diagonal branch, and the air flow velocity of both the adjacent branch and the diagonal branch increases. When closing the air-door on the return side of the adjacent branch, the back-layering rapidly subsided. The wind velocity on the intake side of the adjacent branch is stabilized after a rapid decrease, and the wind velocity of the diagonal branch is stabilized after a rapid increase. When closing the air-door on the intake side of the adjacent branch, the smoke from the diagonal branch spreads. Compared with closing the intake side air-door, closing the air-door on the return side of the adjacent branch is more effective in preventing back-layering. This work provides a reference for preventing back-layering and guiding the evacuation of people from the upstream of a fire source

Can neurological soft signs and neurocognitive deficits serve as a combined endophenotype for Han Chinese with bipolar disorder?

Abstract Background Bipolar disorder's (BD) potential endophenotypes include neurological soft signs (NSS) and neurocognitive disorders (ND). Few research, meanwhile, has coupled NSS and ND as combined endophenotypes of BD. Object This study intends to investigate NSS and ND and compare their differences in euthymic patients with bipolar disorder (EBP), their unaffected first‐degree relatives (FDR), and healthy controls (HC). Additionally, search for potential endophenotypic subprojects of NSS and ND and construct and verify a composite endophenotypic. Methods The subjects were all Han Chinese and consisted of 86 EBP, 81 FDR, and 81HC. Cambridge Neurological Inventory and MATRICSTM Consensus Cognitive Battery tested NSS and ND independently. Results All three groups displayed a trapezoidal distribution of NSS levels and cognitive abnormalities, with EBP having the most severe NSS levels and cognitive deficits, followed by FDR and HC. Among them, motor coordination in NSS and Information processing speed (IPS), Verbal learning (VL), and Working memory (WM) in neurocognitive function are consistent with the traits of the endophenotype of BD. The accuracy in differentiating EBP and HC or FDRs and HC was higher when these items were combined as predictor factors than in differentiating EBP and FDR. Conclusion These results provide more evidence that motor coordination, IPS, VL, and WM may be internal characteristics of bipolar disease. When these characteristics are combined into a complex endophenotype, it may be possible to distinguish BD patients and high‐risk groups from normal populations

LC-MS/MS Analysis Unravels Deep Oxidation of Manganese Superoxide Dismutase in Kidney Cancer

Manganese superoxide dismutase (MNSOD) is one of the major scavengers of reactive oxygen species (ROS) in mitochondria with pivotal regulatory role in ischemic disorders, inflammation and cancer. Here we report oxidative modification of MNSOD in human renal cell carcinoma (RCC) by the shotgun method using data-dependent liquid chromatography tandem mass spectrometry (LC-MS/MS). While 5816 and 5571 proteins were identified in cancer and adjacent tissues, respectively, 208 proteins were found to be up- or down-regulated (p < 0.05). Ontological category, interaction network and Western blotting suggested a close correlation between RCC-mediated proteins and oxidoreductases such as MNSOD. Markedly, oxidative modifications of MNSOD were identified at histidine (H54 and H55), tyrosine (Y58), tryptophan (W147, W149, W205 and W210) and asparagine (N206 and N209) residues additional to methionine. These oxidative insults were located at three hotspots near the hydrophobic pocket of the manganese binding site, of which the oxidation of Y58, W147 and W149 was up-regulated around three folds and the oxidation of H54 and H55 was detected in the cancer tissues only (p < 0.05). When normalized to MNSOD expression levels, relative MNSOD enzymatic activity was decreased in cancer tissues, suggesting impairment of MNSOD enzymatic activity in kidney cancer due to modifications. Thus, LC-MS/MS analysis revealed multiple oxidative modifications of MNSOD at different amino acid residues that might mediate the regulation of the superoxide radicals, mitochondrial ROS scavenging and MNSOD activity in kidney cancer

ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness

Abstract The freshness of vegetable soybean (VS) is an important indicator for quality evaluation. Currently, deep learning-based image recognition technology provides a fast, efficient, and low-cost method for analyzing the freshness of food. The RGB (red, green, and blue) image recognition technology is widely used in the study of food appearance evaluation. In addition, the hyperspectral image has outstanding performance in predicting the nutrient content of samples. However, there are few reports on the research of classification models based on the fusion data of these two sources of images. We collected RGB and hyperspectral images at four different storage times of VS. The ENVI software was adopted to extract the hyperspectral information, and the RGB images were reconstructed based on the downsampling technology. Then, the one-dimensional hyperspectral data was transformed into a two-dimensional space, which allows it to be overlaid and concatenated with the RGB image data in the channel direction, thereby generating fused data. Compared with four commonly used machine learning models, the deep learning model ResNet18 has higher classification accuracy and computational efficiency. Based on the above results, a novel classification model named ResNet-R &H, which is based on the residual networks (ResNet) structure and incorporates the fusion data of RGB and hyperspectral images, was proposed. The ResNet-R &H can achieve a testing accuracy of 97.6%, which demonstrates a significant enhancement of 4.0% and 7.2% compared to the distinct utilization of hyperspectral data and RGB data, respectively. Overall, this research is significant in providing a unique, efficient, and more accurate classification approach in evaluating the freshness of vegetable soybean. The method proposed in this study can provide a theoretical reference for classifying the freshness of fruits and vegetables to improve classification accuracy and reduce human error and variability