Analysis of Noisy Evolutionary Optimization When Sampling Fails

In noisy evolutionary optimization, sampling is a common strategy to deal with noise. By the sampling strategy, the fitness of a solution is evaluated multiple times (called \emph{sample size}) independently, and its true fitness is then approximated by the average of these evaluations. Previous studies on sampling are mainly empirical. In this paper, we first investigate the effect of sample size from a theoretical perspective. By analyzing the (1+1)-EA on the noisy LeadingOnes problem, we show that as the sample size increases, the running time can reduce from exponential to polynomial, but then return to exponential. This suggests that a proper sample size is crucial in practice. Then, we investigate what strategies can work when sampling with any fixed sample size fails. By two illustrative examples, we prove that using parent or offspring populations can be better. Finally, we construct an artificial noisy example to show that when using neither sampling nor populations is effective, adaptive sampling (i.e., sampling with an adaptive sample size) can work. This, for the first time, provides a theoretical support for the use of adaptive sampling

Ethyl 6-amino-5-cyano-2-methyl-4-propyl-4H-pyran-3-carboxyl­ate

The pyran ring of the title compound, C13H18N2O3, is almost planar (r.m.s. deviation = 0.059 Å). The crystal packing is stabilized by N—H⋯O and N—H⋯N hydrogen bonds

How Good Is Neural Combinatorial Optimization?

Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts. Recently, there has been a surge of interest in utilizing Deep Learning, especially Deep Reinforcement Learning, to automatically learn effective solvers for CO. The resultant new paradigm is termed Neural Combinatorial Optimization (NCO). However, the advantages and disadvantages of NCO over other approaches have not been well studied empirically or theoretically. In this work, we present a comprehensive comparative study of NCO solvers and alternative solvers. Specifically, taking the Traveling Salesman Problem as the testbed problem, we assess the performance of the solvers in terms of five aspects, i.e., effectiveness, efficiency, stability, scalability and generalization ability. Our results show that in general the solvers learned by NCO approaches still fall short of traditional solvers in nearly all these aspects. A potential benefit of the former would be their superior time and energy efficiency on small-size problem instances when sufficient training instances are available. We hope this work would help better understand the strengths and weakness of NCO, and provide a comprehensive evaluation protocol for further benchmarking NCO approaches against other approaches

LLM-FuncMapper: Function Identification for Interpreting Complex Clauses in Building Codes via LLM

As a vital stage of automated rule checking (ARC), rule interpretation of regulatory texts requires considerable effort. However, interpreting regulatory clauses with implicit properties or complex computational logic is still challenging due to the lack of domain knowledge and limited expressibility of conventional logic representations. Thus, LLM-FuncMapper, an approach to identifying predefined functions needed to interpret various regulatory clauses based on the large language model (LLM), is proposed. First, by systematically analysis of building codes, a series of atomic functions are defined to capture shared computational logics of implicit properties and complex constraints, creating a database of common blocks for interpreting regulatory clauses. Then, a prompt template with the chain of thought is developed and further enhanced with a classification-based tuning strategy, to enable common LLMs for effective function identification. Finally, the proposed approach is validated with statistical analysis, experiments, and proof of concept. Statistical analysis reveals a long-tail distribution and high expressibility of the developed function database, with which almost 100% of computer-processible clauses can be interpreted and represented as computer-executable codes. Experiments show that LLM-FuncMapper achieve promising results in identifying relevant predefined functions for rule interpretation. Further proof of concept in automated rule interpretation also demonstrates the possibility of LLM-FuncMapper in interpreting complex regulatory clauses. To the best of our knowledge, this study is the first attempt to introduce LLM for understanding and interpreting complex regulatory clauses, which may shed light on further adoption of LLM in the construction domain

Effect of Prunella vulgaris L extract on hyperplasia of mammary gland in rats

Purpose: To explore the effects of Prunella vulgaris L extract (PVE) on hyperplasia of mammary gland (HMG) in rats.Methods: Forty virgin female Wistar rats were randomly divided into normal group, control group (HMG model), positive control group (Rupixiao Capsule, RPXC), and low-, medium- and high-dose (150, 300 and 600 mg/kg) of PVE groups. Injections of estrogen and progestogen were given at the same time to prepare rat. Changes in nipple height were measured, while serum estradiol (E2), progesterone (P), prolactin (PRL), follicle stimulating hormone (FSH) and luteinizing hormone (LH) levels were evaluated by ELISA; Uterus and ovary indices were determined.Results: Compared with control group, PVE reduced elevated nipple height to 2.25 ± 0.09 mm (p < 0.01) and uterus index to 2.29 ± 0.41 mg/g (p < 0.01), as well as reduced the number of mammary gland lobules and secretion in HMG rats. Compared with control group, serum E2 (2.81 ± 0.17 pmol/L), PRL (269.38 ± 8.28 pg/mL) and FSH (0.13 ± 0.03 IU/L) levels (p < 0.01) were lowered, but serum P (1.31 ± 0.13 ng/mL) and LH (1.73 ± 0.08 mIU/mL) levels were higher (p < 0.01) in rats treated with highdose PVE.Conclusion: These results suggest that PVE exerts anti-HMG effect in rats induced by estrogen and progestogen.Keywords: Prunella vulgaris L; Anti-inflammatory; Anti-hyperplasia of mammary glan