DeciLS-PBO: an Effective Local Search Method for Pseudo-Boolean Optimization

Local search is an effective method for solving large-scale combinatorial optimization problems, and it has made remarkable progress in recent years through several subtle mechanisms. In this paper, we found two ways to improve the local search algorithms in solving Pseudo-Boolean Optimization (PBO): Firstly, some of those mechanisms such as unit propagation are merely used in solving MaxSAT before, which can be generalized to solve PBO as well; Secondly, the existing local search algorithms utilize the heuristic on variables, so-called score, to mainly guide the search. We attempt to gain more insights into the clause, as it plays the role of a middleman who builds a bridge between variables and the given formula. Hence, we first extended the combination of unit propagation-based decimation algorithm to PBO problem, giving a further generalized definition of unit clause for PBO problem, and apply it to the existing solver LS-PBO for constructing an initial assignment; then, we introduced a new heuristic on clauses, dubbed care, to set a higher priority for the clauses that are less satisfied in current iterations. Experiments on benchmarks from the most recent PB Competition, as well as three real-world application benchmarks including minimum-width confidence band, wireless sensor network optimization, and seating arrangement problems show that our algorithm DeciLS-PBO has a promising performance compared to the state-of-the-art algorithms

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Value

Model-based diagnosis is a new intelligent diagnostic technique which can overcome the shortcomings of traditional diagnostic methods. In this paper, a novel method of propagating failure value in model-based diagnosis is proposed, and the computing procedure is characterized by combining revised SE-tree (set enumeration tree) with closed nodes to produce all the diagnoses. It can directly compute all minimal diagnoses (MDs), without computing all the conflict sets and therefore the hitting sets of the collection of the corresponding conflict sets like the classical methods, and then the combinatorial explosion caused by calling ATMS, known as an NP-complete problem, can be avoided as well. As the closed nodes are added into the SE-tree, the non-minimal diagnoses can never be produced, and the true resolutions can not be missed by pruning either. The program is easy to be realized, and the diagnosis efficiency is highly improved by this method to satisfy real-time requirement, even for a complex system. Key words: model-based diagnosis; minimal diagnosis; set enumeration tree (SE-tree); failure value 1

Efficient gene deletion and replacement in Aspergillus niger by modified in vivo CRISPR/Cas9 systems

Abstract Aspergillus niger, as an important industrial strain, is widely used in the production of a variety of organic acids and industrial enzymes. To excavate the greater potential of A. niger as a cell factory, the development of highly efficient genome editing techniques is crucial. Here, we developed a modified CRISPR/Cas9 system for A. niger highlighted in two aspects: (1) construction of a single and easy-to-use CRISPR/Cas9 tool plasmid derived from pAN7-1 which is widely used in filamentous fungi; (2) redesign of the easy-to-switch “ribozyme–gRNA–ribozyme (RGR)” element in the tool plasmid. We examined the gene inactivation efficiency without repair fragment and the gene replacement efficiency with repair fragment utilizing the modified system, respectively, and both of them reach the efficiency as high as over 90%. Especially, the co-transformation of the tool plasmid and the specific repair fragment can easily realize one-step knock-out/knock-in of target genes, even with the length of homologous arms as only 100 bp. The establishment of this system will lay a solid foundation for the gene function research and rational design of cell factory in A. niger or broader filamentous fungi hosts

An improved wavelet threshold denoising approach for surface electromyography signal

Abstract Background The surface electromyography (sEMG) signal presents significant challenges for the dynamic analysis and subsequent examination of muscle movements due to its low signal energy, broad frequency distribution, and inherent noise interference. However, the conventional wavelet threshold filtering techniques for sEMG signals are plagued by the Gibbs-like phenomenon and an overall decrease in signal amplitude, leading to signal distortion. Purpose This article aims to establish an improved wavelet thresholding method that can filter various types of signals, with a particular emphasis on sEMG signals, by adjusting two independent factors. Hence, it generates the filtered signal with a higher signal-to-noise ratio (SNR), a lower mean square error (MSE), and better signal quality. Results After denoising Doppler and Heavysine signals, the filtered signal exhibits a higher SNR and lower MSE than the signal generated from traditional filtering algorithms. The filtered sEMG signal has a lower noise baseline while retaining the peak sEMG signal strength. Conclusion The empirical evaluation results show that the quality of the signal processed by the new noise reduction algorithm is better than the traditional hard thresholding, soft thresholding, and Garrote thresholding methods. Moreover, the filtering performance on the sEMG signal is improved significantly, which enhances the accuracy and reliability of subsequent experimental analyses