2,282 research outputs found
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-carboxylÂate
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
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