On How to Improve Cultural Education Ability of Instructors

Instructors need to enhance their ability of educating people with culture in order to cultivate young innovative talents, revolutionize instructors’ work and achieve long-term professional development. Cultural education consistsof ideological and political education. It also consists with the duty of instructors. This makes possible instructors’ cultural education. Improving instructors’ ability of educating people with culture depends on a clear understanding of its connotation, characteristics and structure as well as on an exploration of practical strategies in terms of how to inspire new ideas, nurture good qualities and set up platforms

Impression Effect vs. Click-through Effect: Mechanism Design of Online Advertising

Search advertising and display advertising are two major online advertising formats. Search advertising emphasizes ads’ click-through effect. Advertisers only pay when users click the link of their ads. Traditional display advertising emphasizes ads’ impression effect. Most display ads are charged based on the number of views on the ads. Considering that most online ads increase brand awareness (impression effect) and directly promote sales (click-through effect), the not-emphasized effect in search advertising or display advertising actually has a significant impact on the market outcome. However, these impacts have been largely ignored. In this paper, we examine various mechanisms in search and display advertising by considering both ads’ impression effect and click-through effect. Interestingly, we show a seesaw relationship between ads’ two effects in search advertising. The advertiser whose advertisement has a strong click-through effect benefits relatively less from its impression effect. In display advertising, the real-time-bidding (RTB) mechanism considers both ads’ impression effect and click-through effect. It allows a publisher to gain more surplus than that through a static auction. However, we show that RTB is associated with a high risk of market failure

Model Checking Temporal Logic Formulas Using Sticker Automata

As an important complex problem, the temporal logic model checking problem is still far from being fully resolved under the circumstance of DNA computing, especially Computation Tree Logic (CTL), Interval Temporal Logic (ITL), and Projection Temporal Logic (PTL), because there is still a lack of approaches for DNA model checking. To address this challenge, a model checking method is proposed for checking the basic formulas in the above three temporal logic types with DNA molecules. First, one-type single-stranded DNA molecules are employed to encode the Finite State Automaton (FSA) model of the given basic formula so that a sticker automaton is obtained. On the other hand, other single-stranded DNA molecules are employed to encode the given system model so that the input strings of the sticker automaton are obtained. Next, a series of biochemical reactions are conducted between the above two types of single-stranded DNA molecules. It can then be decided whether the system satisfies the formula or not. As a result, we have developed a DNA-based approach for checking all the basic formulas of CTL, ITL, and PTL. The simulated results demonstrate the effectiveness of the new method

A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data

Symbolic regression (SR) is a powerful technique for discovering the underlying mathematical expressions from observed data. Inspired by the success of deep learning, recent efforts have focused on two categories for SR methods. One is using a neural network or genetic programming to search the expression tree directly. Although this has shown promising results, the large search space poses difficulties in learning constant factors and processing high-dimensional problems. Another approach is leveraging a transformer-based model training on synthetic data and offers advantages in inference speed. However, this method is limited to fixed small numbers of dimensions and may encounter inference problems when given data is out-of-distribution compared to the synthetic data. In this work, we propose DySymNet, a novel neural-guided Dynamic Symbolic Network for SR. Instead of searching for expressions within a large search space, we explore DySymNet with various structures and optimize them to identify expressions that better-fitting the data. With a topology structure like neural networks, DySymNet not only tackles the challenge of high-dimensional problems but also proves effective in optimizing constants. Based on extensive numerical experiments using low-dimensional public standard benchmarks and the well-known SRBench with more variables, our method achieves state-of-the-art performance in terms of fitting accuracy and robustness to noise

Heat Transfer Analysis of MgB₂ Coil in Heat Treatment Process for Future Fusion Reactor

State of the art MgB2 is reviewed as a potential material for the poloidal field (PF) coils of the future fusion reactor due to its high critical temperature and low material cost. The heat treatment process is a crucial step in the development of MgB2 magnets. The temperature lag in heat treatment of large magnets can lead to insufficient thermal reaction time. It may be infeasible to control the temperature of a magnet according to the heat treatment scheme recommended for the MgB2 wire. Hence, the heat treatment process of a large magnet needs to be evaluated. Therefore, the dynamic temperature distribution of a MgB2 PF coil is obtained by simulating the heat transfer in heat treatment process. A suitable heat treatment schedule for a large magnet is proposed and the experimental results of a sub-size Cable-In-Conduit Conductor manufactured with MgB2 strand confirmed the feasibility of the newly proposed heat treatment process. The results provide a reference for the heat treatment method of a future larger MgB2 coil.</p