LEA: Beyond Evolutionary Algorithms via Learned Optimization Strategy

Evolutionary algorithms (EAs) have emerged as a powerful framework for expensive black-box optimization. Obtaining better solutions with less computational cost is essential and challenging for black-box optimization. The most critical obstacle is figuring out how to effectively use the target task information to form an efficient optimization strategy. However, current methods are weak due to the poor representation of the optimization strategy and the inefficient interaction between the optimization strategy and the target task. To overcome the above limitations, we design a learned EA (LEA) to realize the move from hand-designed optimization strategies to learned optimization strategies, including not only hyperparameters but also update rules. Unlike traditional EAs, LEA has high adaptability to the target task and can obtain better solutions with less computational cost. LEA is also able to effectively utilize the low-fidelity information of the target task to form an efficient optimization strategy. The experimental results on one synthetic case, CEC 2013, and two real-world cases show the advantages of learned optimization strategies over human-designed baselines. In addition, LEA is friendly to the acceleration provided by Graphics Processing Units and runs 102 times faster than unaccelerated EA when evolving 32 populations, each containing 6400 individuals

Benchmarking Software Vulnerability Detection Techniques: A Survey

Software vulnerabilities can have serious consequences, which is why many techniques have been proposed to defend against them. Among these, vulnerability detection techniques are a major area of focus. However, there is a lack of a comprehensive approach for benchmarking these proposed techniques. In this paper, we present the first survey that comprehensively investigates and summarizes the current state of software vulnerability detection benchmarking. We review the current literature on benchmarking vulnerability detection, including benchmarking approaches in technique-proposing papers and empirical studies. We also separately discuss the benchmarking approaches for traditional and deep learning-based vulnerability detection techniques. Our survey analyzes the challenges of benchmarking software vulnerability detection techniques and the difficulties involved. We summarize the challenges of benchmarking software vulnerability detection techniques and describe possible solutions for addressing these challenges

Seasonal and intra-seasonal thermocline variability in the central South China Sea

Geophysical Research Letters, American Geophysical UnionSeasonal and intraseasonal variability of thermocline and relative surface height in the central South China Sea (SCS) are investigated using time series data of temperature from three buoys and sea surface height anomaly data from TOPEX/POSEIDON and ERS-1/ERS-2 satellites( T/P-ERS) from Feb. 1998 through Mar. 1999. We found that the thermocline becomesde eper and thinner in winter, owing to a great loss of the heat on the sea surface. Thisf eature is more evident in the northern than the southern part of the central SCS. The intraseasonal variation of the thermocline ismain ly controlled by the geostrophic vorticity and is out-of-phase with sea surface height (SSH). Furthermore, we find a double-thermocline phenomenon occurs in the SCS: In spring, owing to maximum net downward heat flux at the surface, with the new thermocline appearing above 80 m and the old thermocline keeping under 80 m deep

Robust Test for Spatial Error Model:Considering Changes of Spatial Layouts and Distribution Misspecification

This paper suggests a robust LM (Lagrange Multiplier) test for spatial error model which not only reduces the influence of spatial lag dependence immensely, but also presents robust to changes of spatial layouts and distribution misspecification. Monte Carlo simulation results imply that existing LM tests have serious size and power distortion with the presence of spatial lag dependence, group interaction or non-normal distribution, but the robust LM test of this paper shows well performance

Robust Test for Spatial Error Model:Considering Changes of Spatial Layouts and Distribution Misspecification

This paper suggests a robust LM (Lagrange Multiplier) test for spatial error model which not only reduces the influence of spatial lag dependence immensely, but also presents robust to changes of spatial layouts and distribution misspecification. Monte Carlo simulation results imply that existing LM tests have serious size and power distortion with the presence of spatial lag dependence, group interaction or non-normal distribution, but the robust LM test of this paper shows well performance

Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation

Advanced recommender systems usually involve multiple domains (scenarios or categories) for various marketing strategies, and users interact with them to satisfy their diverse demands. The goal of multi-domain recommendation is to improve the recommendation performance of all domains simultaneously. Conventional graph neural network based methods usually deal with each domain separately, or train a shared model for serving all domains. The former fails to leverage users' cross-domain behaviors, making the behavior sparseness issue a great obstacle. The latter learns shared user representation with respect to all domains, which neglects users' domain-specific preferences. These shortcomings greatly limit their performance in multi-domain recommendation. To tackle the limitations, an appropriate way is to learn from multi-domain user feedbacks and obtain separate user representations to characterize their domain-specific preferences. In this paper we propose $\mathsf{H^3Trans}$, a hierarchical hypergraph network based correlative preference transfer framework for multi-domain recommendation. $\mathsf{H^3Trans}$ represents multi-domain feedbacks into a unified graph to help preference transfer via taking full advantage of users' multi-domain behaviors. We incorporate two hyperedge-based modules, namely dynamic item transfer module (Hyper-I) and adaptive user aggregation module (Hyper-U). Hyper-I extracts correlative information from multi-domain user-item feedbacks for eliminating domain discrepancy of item representations. Hyper-U aggregates users' scattered preferences in multiple domains and further exploits the high-order (not only pair-wise) connections among them to learn user representations. Experimental results on both public datasets and large-scale production datasets verify the superiority of $\mathsf{H^3Trans}$ for multi-domain recommendation.Comment: Work in progres

Robust test for spatial error model:considering changes of spatial layouts and distribution misspecification

This paper suggests a robust LM (Lagrange Multiplier) test for spatial error model which not only reduces the influence of spatial lag dependence immensely, but also presents robust to changes of spatial layouts and distribution misspecification. Monte Carlo simulation results imply that existing LM tests have serious size and power distortion with the presence of spatial lag dependence, group interaction or non-normal distribution, but the robust LM test of this paper shows well performance

Robust test for spatial error model:considering changes of spatial layouts and distribution misspecification

This paper suggests a robust LM (Lagrange Multiplier) test for spatial error model which not only reduces the influence of spatial lag dependence immensely, but also presents robust to changes of spatial layouts and distribution misspecification. Monte Carlo simulation results imply that existing LM tests have serious size and power distortion with the presence of spatial lag dependence, group interaction or non-normal distribution, but the robust LM test of this paper shows well performance