Open the Black Box – Visualising CNN to Understand Its Decisions on Road Network Performance Level

Visualisation helps explain the operating mechanisms of deep learning models, but its applications are rarely seen in traffic analysis. This paper employs a convolu-tional neural network (CNN) to evaluate road network performance level (NPL) and visualises the model to en-lighten how it works. A dataset of an urban road network covering a whole year is used to produce performance maps to train a CNN. In this process, a pretrained network is introduced to overcome the common issue of inadequa-cy of data in transportation research. Gradient weighted class activation mapping (Grad-CAM) is applied to vi-sualise the CNN, and four visualisation experiments are conducted. The results illustrate that the CNN focuses on different areas when it identifies the road network as dif-ferent NPLs, implying which region contributes the most to the deteriorating performance. There are particular visual patterns when the road network transits from one NPL to another, which may help performance prediction. Misclassified samples are analysed to determine how the CNN fails to make the right decisions, exposing the model’s deficiencies. The results indicate visualisation’s potential to contribute to comprehensive management strategies and effective model improvement

Study of Implementation of CNN on Low-power Platform for Smart Traffic Optimization

Accompanied with the rise of smart city and the development of IoT (Internet of Things), people are looking forward to monitoring and regulating the traffic in a smarter way. Since the deep neural network has shown its great value in vehicle detection area, people may wonder what kind of impact would be brought by the combination of IoT and deep learning techniques. In this work, an exploration of implementation of CNN (convolutional neural network) on low-power platform for smart traffic optimization has been conducted. During the research, a new optimization approach, which aims at S-CNN (Sparse Convolutional Neural Network) optimization from architecture level, has been proposed; and outstanding performance has been obtained when compared to mainstream deep learning frameworks, such as Tensorflow. In the experiments, the new proposed S-CNN optimization approach is as twice fast as Tensorflow on 94% sparse model and becomes 5 times faster on 98% sparse model. Besides, the author also verified the feasibility of real-time CNN implementation on ARM platform and Jetson TX1 embedded system, which reveals the shortage of computational resource on ARM platform and the potential of Jetson series to become the low-power platform for CNN implementation

On Evaluating Circuits with Inputs Encrypted by Different Fully Homomorphic Encryption Schemes

We consider the problem of evaluating circuits whose inputs are encrypted with possibly different encryption schemes. Let $\mathcal{C}$ be any circuit with input $x_1, \dots, x_t \in \{0,1\}$, and let $\mathcal{E}_i$, $1 \le i \le t$, be (possibly) different fully homomorphic encryption schemes, whose encryption algorithms are \Enc_i. Suppose $x_i$ is encrypted with $\mathcal{E}_i$ under a public key $pk_i$, say c_i \leftarrow \Enc_i({pk_i}, x_i). Is there any algorithm \Evaluate such that \Evaluate(\mathcal{C}, \langle \mathcal{E}_1, pk_1, c_1\rangle, \dots, \langle \mathcal{E}_t, pk_t, c_t\rangle) returns a ciphertext $c$ that, once decrypted, equals $\mathcal{C}(x_1, \dots, x_t)$? We propose a solution to this seemingly impossible problem with the number of different schemes and/or keys limited to a small value. Our result also provides a partial solution to the open problem of converting any FHE scheme to a multikey FHE scheme

Unified framework of the microscopic Landau-Lifshitz-Gilbert equation and its application to Skyrmion dynamics

The Landau-Lifshitz-Gilbert (LLG) equation is widely used to describe magnetization dynamics. We develop a unified framework of the microscopic LLG equation based on the nonequilibrium Green's function formalism. We present a unified treatment for expressing the microscopic LLG equation in several limiting cases, including the adiabatic, inertial, and nonadiabatic limits with respect to the precession frequency for a magnetization with fixed magnitude, as well as the spatial adiabatic limit for the magnetization with slow variation in both its magnitude and direction. The coefficients of those terms in the microscopic LLG equation are explicitly expressed in terms of nonequilibrium Green's functions. As a concrete example, this microscopic theory is applied to simulate the dynamics of a magnetic Skyrmion driven by quantum parametric pumping. Our work provides a practical formalism of the microscopic LLG equation for exploring magnetization dynamics

Expression and Clinical Relevance of uPA and ET-1 in Non-small Cell Lung Cancer

Background and objective uPA and ET-1 proteins have been reported to be up-regulated in some of human cancers. The aim of this study is to investigate the alteration and clinical relevance of uPA and ET-1 protein levels in non-small cell lung cancer (NSCLC). Methods Expressions of uPA and ET-1 protein were detected in 155 cases of NSCLC with tissue microarrays and immunohistochemistry (TMA-IHC) technique. The correlations between the alteration of the two proteins and clinicopathological parameters were analyzed. Results Negative/weak, moderate and high expression of uPA were observed in 12.3%, 64.4% and 23.3% of squamous cell carcinomas, in 12.2%, 53.7% and 34.1% of adenocarcinomas, and in 12.3%, 58.7% and 29.0% of all cases. ET-1 presented negative/weak, moderate and high expression in 2.7%, 42.5% and 54.8% of squamous cell carcinomas, in 11.0%, 30.5% and 58.5% of adenocarcinomas, and in 7.1%, 36.1% and 56.8% of all cases. Simultaneously high expression of uPA and ET-1 were found in adenocarcinomas without lymph node metastasis (P=0.017). Adenocarcinoma patients with high expression of uPA or with high expression of both ET-1 and uPA had the longer survival time (P=0.007 and 0.016). Conclusion Detection of uPA and ET-1 protein levels might contribute to the prognosis evaluation of NSCLC

The relationship between childhood trauma and Internet gaming disorder among college students: A structural equation model

open access journalBackground The aim of this study was to investigate the mechanisms of Internet gaming disorder (IGD) and the associated interaction effects of childhood trauma, depression and anxiety in college students. Methods Participants were enrolled full-time as freshmen at a University in the Hunan province, China. All participants reported their socio-demographic characteristics and undertook a standardized assessment on childhood trauma, anxiety, depression and IGD. The effect of childhood trauma on university students' internet gaming behaviour mediated by anxiety and depression was analysed using structural equation modelling (SEM) using R 3.6.1. Results In total, 922 freshmen participated in the study, with an approximately even male-to-female ratio. A mediation model with anxiety and depression as the mediators between childhood trauma and internet gaming behaviour allowing anxiety and depression to be correlated was tested using SEM. The SEM analysis revealed that a standardised total effect of childhood trauma on Internet gaming was 0.18, (Z = 5.60, 95% CI [0.02, 0.05], P < 0.001), with the direct effects of childhood trauma on Internet gaming being 0.11 (Z = 3.41, 95% CI [0.01, 0.03], P = 0.001), and the indirect effects being 0.02 (Z = 2.32, 95% CI [0.00, 0.01], P = 0.020) in the pathway of childhood trauma-depression-internet gaming; and 0.05 (Z = 3.67, 95% CI [0.00, 0.02], P < 0.001) in the pathway of childhood trauma-anxiety-Internet gaming. In addition, the two mediators anxiety and depression were significantly correlated (r = 0.50, Z = 13.54, 95% CI [3.50, 5.05], P < 0.001). Conclusions The study revealed that childhood trauma had a significant impact on adolescents' Internet gaming behaviours among college students. Anxiety and depression both significantly mediated the relationship between childhood trauma and internet gaming and augmented its negative influence. Discussion of the need to understand the subtypes of childhood traumatic experience in relationship to addictive behaviours is included

Improved active disturbance rejection controller for rotor system of magnetic levitation turbomachinery

The rotor of the magnetic suspension turbomachinery is supported by the magnetic suspension bearing without contact and mechanical friction, which directly drives the high-efficiency fluid impeller. It has the advantages of high efficiency, low noise, less fault and no lubrication. However, the system often has some unknown mutation, time variation, load perturbation and other un-certainties when working, and the traditional Proportion Integration Differentiation (PID) control strategy has great limitations to overcome the above disturbances. Therefore, this paper firstly establishes a mathematical model of the rotor of magnetic levitation turbomachinery. Then, a linear active disturbance rejection controller (LADRC) is presented, which can not only improve the above problems of PID control, but also avoid the complex parameter tuning process of traditional nonlinear active disturbance rejection control (ADRC). However, LADRC is easy to induce the overshoot of the system and cannot filter the given signal. On this basis, an improved LADRC with a fast-tracking differentiator (FTD) is proposed to arrange the transition process of input signals. The simulation results show that compared with the traditional PID controller and single LADRC, the improved linear active disturbance rejection control method with fast tracking differentiator (FTD-LADRC) can better suppress some unknown abrupt changes, time variation and other uncertainties of the electromagnetic bearing-rotor system. At the same time, the overshoot of the system is smaller, and the parameters are easy to be set, which is convenient for engineering application

Soil Moisture Retrieval Using BuFeng-1 A/B Based on Land Surface Clustering Algorithm

A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm3·cm-3. The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm3·cm-3. This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 41971377). China Spacesat Company, Ltd. ESA-MOST China Dragon5 Programme (Grant Number: ID.58070) 10.13039/501100003392-Natural Science Foundation of Fujian Province (Grant Number: 2019J01853