Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks

As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a variety of fields, there is an increasing interest in understanding the complex internal mechanisms of DNNs. In this paper, we propose Relative Attributing Propagation (RAP), which decomposes the output predictions of DNNs with a new perspective of separating the relevant (positive) and irrelevant (negative) attributions according to the relative influence between the layers. The relevance of each neuron is identified with respect to its degree of contribution, separated into positive and negative, while preserving the conservation rule. Considering the relevance assigned to neurons in terms of relative priority, RAP allows each neuron to be assigned with a bi-polar importance score concerning the output: from highly relevant to highly irrelevant. Therefore, our method makes it possible to interpret DNNs with much clearer and attentive visualizations of the separated attributions than the conventional explaining methods. To verify that the attributions propagated by RAP correctly account for each meaning, we utilize the evaluation metrics: (i) Outside-inside relevance ratio, (ii) Segmentation mIOU and (iii) Region perturbation. In all experiments and metrics, we present a sizable gap in comparison to the existing literature. Our source code is available in \url{https://github.com/wjNam/Relative_Attributing_Propagation}.Comment: 8 pages, 7 figures, Accepted paper in AAAI Conference on Artificial Intelligence (AAAI), 202

Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations

The clear transparency of Deep Neural Networks (DNNs) is hampered by complex internal structures and nonlinear transformations along deep hierarchies. In this paper, we propose a new attribution method, Relative Sectional Propagation (RSP), for fully decomposing the output predictions with the characteristics of class-discriminative attributions and clear objectness. We carefully revisit some shortcomings of backpropagation-based attribution methods, which are trade-off relations in decomposing DNNs. We define hostile factor as an element that interferes with finding the attributions of the target and propagate it in a distinguishable way to overcome the non-suppressed nature of activated neurons. As a result, it is possible to assign the bi-polar relevance scores of the target (positive) and hostile (negative) attributions while maintaining each attribution aligned with the importance. We also present the purging techniques to prevent the decrement of the gap between the relevance scores of the target and hostile attributions during backward propagation by eliminating the conflicting units to channel attribution map. Therefore, our method makes it possible to decompose the predictions of DNNs with clearer class-discriminativeness and detailed elucidations of activation neurons compared to the conventional attribution methods. In a verified experimental environment, we report the results of the assessments: (i) Pointing Game, (ii) mIoU, and (iii) Model Sensitivity with PASCAL VOC 2007, MS COCO 2014, and ImageNet datasets. The results demonstrate that our method outperforms existing backward decomposition methods, including distinctive and intuitive visualizations.Comment: 9 pages, 8 figures, Accepted paper in AAAI Conference on Artificial Intelligence (AAAI), 202

Multiple Sensor Fusion and Motion Control of Snake Robot Based on Soft-Computing

There are many circumstance limits to human like extreme radioactivity, temperature

Adaptive Noise Reduction Algorithm to Improve R Peak Detection in ECG Measured by Capacitive ECG Sensors

Electrocardiograms (ECGs) can be conveniently obtained using capacitive ECG sensors. However, motion noise in measured ECGs can degrade R peak detection. To reduce noise, properties of reference signal and ECG measured by the sensors are analyzed and a new method of active noise cancellation (ANC) is proposed in this study. In the proposed algorithm, the original ECG signal at QRS interval is regarded as impulsive noise because the adaptive filter updates its weight as if impulsive noise is added. As the proposed algorithm does not affect impulsive noise, the original signal is not reduced during ANC. Therefore, the proposed algorithm can conserve the power of the original signal within the QRS interval and reduce only the power of noise at other intervals. The proposed algorithm was verified through comparisons with recent research using data from both indoor and outdoor experiments. The proposed algorithm will benefit a noise reduction of noisy biomedical signal measured from sensors.11Ysciescopu

The Fruit Hull of Gleditsia sinensis

Lung cancer has substantial mortality worldwide, and chemotherapy is a routine regimen for the treatment of patients with lung cancer, despite undesirable effects such as drug resistance and chemotoxicity. Here, given a possible antitumor effect of the fruit hull of Gleditsia sinensis (FGS), we tested whether FGS enhances the effectiveness of cis-diammine dichloridoplatinum (II) (CDDP), a chemotherapeutic drug. We found that CDDP, when administered with FGS, significantly decreased the viability and increased the apoptosis and cell cycle arrest of Lewis lung carcinoma (LLC) cells, which were associated with the increase of p21 and decreases of cyclin D1 and CDK4. Concordantly, when combined with FGS, CDDP significantly reduced the volume and weight of tumors derived from LLC subcutaneously injected into C57BL/6 mice, with concomitant increases of phosphor-p53 and p21 in tumor tissue. Together, these results show that FGS could enhance the antitumor activity of CDDP, suggesting that FGS can be used as a complementary measure to enhance the efficacy of a chemotherapeutic agent such as CDDP