GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction

Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new targets. However, the performance of previous computational models is limited by the following drawbacks. The learning of drug representation relies only on supervised data, without taking into account the information contained in the molecular graph itself. Moreover, most previous studies tended to design complicated representation learning module, while uniformity, which is used to measure representation quality, is ignored. In this study, we propose GraphCL-DTA, a graph contrastive learning with molecular semantics for drug-target binding affinity prediction. In GraphCL-DTA, we design a graph contrastive learning framework for molecular graphs to learn drug representations, so that the semantics of molecular graphs are preserved. Through this graph contrastive framework, a more essential and effective drug representation can be learned without additional supervised data. Next, we design a new loss function that can be directly used to smoothly adjust the uniformity of drug and target representations. By directly optimizing the uniformity of representations, the representation quality of drugs and targets can be improved. The effectiveness of the above innovative elements is verified on two real datasets, KIBA and Davis. The excellent performance of GraphCL-DTA on the above datasets suggests its superiority to the state-of-the-art model.Comment: 13 pages, 4 figures, 5 table

Robust passivity-based dynamical systems for compliant motion adaptation

Motivated by human compliant behaviors during interacting with unknown environments and how motions and impedance to are adapted skilfully complete a task, this article develops a motion planning scheme that is capable of generating a compliant trajectory online such that tracking desired contacting forces under a predefined motion task. First, an improved dynamical system (DS) is designed to generate an adaptive compliant scanning trajectory online from the original DS in terms of the contact forces and the desired scanning forces. Inspired by passivity analysis for the robot control system, a robust term is formulated to guarantee stability by considering the balance between environmental and robotic energy. Furthermore, we develop a state-constrained controller based on barrier Lyapunov function to track the compliant DS motion and to ensure safety during scanning for the patient. Finally, comparative simulations are conducted to validate the general compliant capability of the proposed framework. We also instantiate our methodology through a use case of liver ultrasound scanning to demonstrate the stable and dynamic force tracking performance

A Short-Term Wind Speed Forecasting Hybrid Model Based on Empirical Mode Decomposition and Multiple Kernel Learning

Short-term wind speed forecasting plays an increasingly important role in the security, scheduling, and optimization of power systems. As wind speed signals are usually nonlinear and nonstationary, how to accurately forecast future states is a challenge for existing methods. In this paper, for highly complex wind speed signals, we propose a multiple kernel learning- (MKL-) based method to adaptively assign the weights of multiple prediction functions, which extends conventional wind speed forecasting methods using a support vector machine. First, empirical mode decomposition (EMD) is used to decompose complex signals into several intrinsic mode function component signals with different time scales. Then, for each channel, one multiple kernel model is constructed for forecasting the current sequence signal. Finally, several experiments are carried out on different New Zealand wind farm data, and the relevant prediction accuracy indexes and confidence intervals are evaluated. Extensive experimental results show that, compared with existing machine learning methods, the EMD-MKL model proposed in this paper has better performance in terms of the prediction accuracy evaluation indexes and confidence intervals and shows a better ability to generalize

Price-setting based combinatorial auction approach for carrier collaboration with pickup and delivery requests

International audienceA carrier collaboration problem with pickup and delivery requests is considered, in which multiple carriers form an alliance to share their pickup and delivery requests and vehicle capacities in order to reduce their transportation costs and consequently increase their profits. A multi-round pricing-setting based combinatorial auction approach is proposed to solve the problem. In each round of the auction, the auctioneer updates the price for serving each request based on Lagrangian relaxation, and each carrier, who is a bidder, determines its requests to be outsourced and the requests to be acquired from other carriers by solving a request selection problem based on the prices. Different price adjustment methods are proposed and compared. Numerical experiments on randomly generated instances demonstrate the effectiveness of the approach

Online Estimation of Supercapacitor State of Charge Based on Nonlinear Observer

Supercapacitors have the advantages of fast charging and discharging, high power density, and long life, which are widely used in energy storage systems for new energy vehicles. Reliable operation of the system requires the acquisition of its remaining electricity, which is to estimate its state of charge (SOC). Relying on the equivalent analog circuit model of a single supercapacitor, this paper establishes a state-space model of the capacitor second-order nonlinear system with the multi-capacitor terminal voltage in the model as the state, the capacitor input current as the control input, and the capacitor output voltage as the observation output, and contains the leakage current caused by the self-discharge phenomenon. In order to improve the simulation accuracy, different model parameters were identified to characterize the charging and discharging conditions. In this paper, a nonlinear observer algorithm is used to obtain the internal state of the model to realize the estimation of SOC. The results of the charging and discharging experiment show that considering the leakage factor and establishing the charging and discharging model with different parameters, the dynamic characteristics of the supercapacitor can be better simulated, and the nonlinear observer algorithm has a stable tracking ability

An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10−6  level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance