Taylor Genetic Programming for Symbolic Regression

Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR) problems. Compared with the machine learning or deep learning methods that depend on the pre-defined model and the training dataset for solving SR problems, GP is more focused on finding the solution in a search space. Although GP has good performance on large-scale benchmarks, it randomly transforms individuals to search results without taking advantage of the characteristics of the dataset. So, the search process of GP is usually slow, and the final results could be unstable. To guide GP by these characteristics, we propose a new method for SR, called Taylor genetic programming (TaylorGP). TaylorGP leverages a Taylor polynomial to approximate the symbolic equation that fits the dataset. It also utilizes the Taylor polynomial to extract the features of the symbolic equation: low order polynomial discrimination, variable separability, boundary, monotonic, and parity. GP is enhanced by these Taylor polynomial techniques. Experiments are conducted on three kinds of benchmarks: classical SR, machine learning, and physics. The experimental results show that TaylorGP not only has higher accuracy than the nine baseline methods, but also is faster in finding stable results

Experimental measurement of the quantum geometric tensor using coupled qubits in diamond

Geometry and topology are fundamental concepts, which underlie a wide range of fascinating physical phenomena such as topological states of matter and topological defects. In quantum mechanics, the geometry of quantum states is fully captured by the quantum geometric tensor. Using a qubit formed by an NV center in diamond, we perform the first experimental measurement of the complete quantum geometric tensor. Our approach builds on a strong connection between coherent Rabi oscillations upon parametric modulations and the quantum geometry of the underlying states. We then apply our method to a system of two interacting qubits, by exploiting the coupling between the NV center spin and a neighboring $^{13}$C nuclear spin. Our results establish coherent dynamical responses as a versatile probe for quantum geometry, and they pave the way for the detection of novel topological phenomena in solid state

Wavelet-based variability of Yellow River discharge at 500-. 100-, and 50-year timescales

Water scarcity in the Yellow River, China, has become increasingly severe over the past half century. In this paper, wavelet transform analysis was used to detect the variability of natural, observed, and reconstructed streamflow in the Yellow River at 500-, 100-, and 50-year timescales. The periodicity of the streamflow series and the co-varying relationships between streamflow and atmospheric circulation indices/sunspot number were assessed by means of continuous wavelet transform (CWT) and wavelet transform coherence (WTC) analyses. The CWT results showed intermittent oscillations in streamflow with increasing periodicities of 1–6 years at all timescales. Significant multidecadal and century-scale periodicities were identified in the 500-year streamflow series. The WTC results showed intermittent interannual covariance of streamflow with atmospheric circulation indices and sunspots. At the 50-year timescale, there were significant decadal oscillations between streamflow and the Arctic Oscillation (AO) and the Pacific Decadal Oscillation (PDO), and bidecadal oscillations with the PDO. At the 100-year timescale, there were significant decadal oscillations between streamflow and Niño 3.4, the AO, and sunspots. At the 500-year timescale, streamflow in the middle reaches of the Yellow River showed prominent covariance with the AO with an approximately 32-year periodicity, and with sunspots with an approximately 80-year periodicity. Atmospheric circulation indices modulate streamflow by affecting temperature and precipitation. Sunspots impact streamflow variability by influencing atmospheric circulation, resulting in abundant precipitation. In general, for both the CWT and the WTC results, the periodicities were spatially continuous, with a few gradual changes from upstream to downstream resulting from the varied topography and runoff. At the temporal scale, the periodicities were generally continuous over short timescales and discontinuous over longer timescales

Effect of Volatile Compounds from Bacillus subtilis PW2 against Aspergillus ochraceus

The composition of antifungal volatile compounds (VC) produced by Bacillus subtilis PW2 was identified by solid phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS). Then, the identified single components were selected to determine their inhibitory effect against Aspergillus ochraceus by plate buckling method, and the effect and mechanism of the most active VC on the growth and toxicity of A. ochraceu. The results showed that 41 components including esters, aldehydes, alkanes, alcohols, ketones, acids and olefins were identified in the VC produced by PW2. Among these compounds, 2-ethyl hexanol (2-EH) had the strongest inhibitory activity against A. ochraceus. Direct contact with 2-EH at a dose of 1 562.5 μL/L completely inhibited the growth of A. ochraceus and reduced the content of ochratoxin A (OTA) by 23.67%. 2-EH vapor at doses of 112 and 281 μL/L completely inhibited and killed A. ochraceus, respectively. The spores of A. ochraceus treated with 2-EH appeared wrinkled, sunken and shriveled, and the integrity of the cell membrane was destroyed. Furthermore, the mycelial ergosterol content decreased by 42.68%–65.40%, and nucleic acid and protein leaked out of the cells after this treatment. This study shows that 2-EH can inhibit and kill A. ochraceus by destroying its cell membrane, which provides a theoretical basis for the application of 2-EH in the prevention of food mildew

Real-time Quality Inspection of Motor Rotor Using Cost-effective Intelligent Edge System

Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99%. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection

First identification of long non-coding RNAs in fungal parasite Nosema ceranae

International audienceAbstractNosema ceranae is a unicellular fungal parasite of honey bees and causes huge losses for apiculture. Until present, no study on N. ceranae long non-coding RNAs (lncRNAs) was documented. Here, we sequenced purified spores of N. ceranae using strand-specific library construction and high-throughput RNA sequencing technologies. In total, 83 novel lncRNAs were predicted from N. ceranae spore samples, including lncRNAs, long intergenic non-coding RNAs (lincRNAs), and sense lncRNAs. Moreover, these lncRNAs share similar characteristics with those identified in mammals and plants, such as shorter length and fewer exon number and transcript isoforms than protein-coding genes. Finally, the expression of 12 lncRNAs was confirmed with RT-PCR, confirming their true existence. To our knowledge, this is the first evidence of lncRNAs produced by a microsporidia species, offering novel insights into basic biology such as regulation of gene expression of this widespread taxonomic group

PACE Solver Description: Hust-Solver - A Heuristic Algorithm of Directed Feedback Vertex Set Problem

A directed graph is formed by vertices and arcs from one vertex to another. The feedback vertex set problem (FVSP) consists in making a given directed graph acyclic by removing as few vertices as possible. In this write-up, we outline the core techniques used in the heuristic feedback vertex set algorithm, submitted to the heuristic track of the 2022 PACE challenge

Biogeographic patterns of soil diazotrophic communities across six forests in North America.

Soil diazotrophs play important roles in ecosystem functioning by converting atmospheric N2 into biologically available ammonium. However, the diversity and distribution of soil diazotrophic communities in different forests and whether they follow biogeographic patterns similar to macroorganisms still remain unclear. By sequencing nifH gene amplicons, we surveyed the diversity, structure and biogeographic patterns of soil diazotrophic communities across six North American forests (126 nested samples). Our results showed that each forest harboured markedly different soil diazotrophic communities and that these communities followed traditional biogeographic patterns similar to plant and animal communities, including the taxa-area relationship (TAR) and latitudinal diversity gradient. Significantly higher community diversity and lower microbial spatial turnover rates (i.e. z-values) were found for rainforests (~0.06) than temperate forests (~0.1). The gradient pattern of TARs and community diversity was strongly correlated (r(2)  > 0.5) with latitude, annual mean temperature, plant species richness and precipitation, and weakly correlated (r(2)  < 0.25) with pH and soil moisture. This study suggests that even microbial subcommunities (e.g. soil diazotrophs) follow general biogeographic patterns (e.g. TAR, latitudinal diversity gradient), and indicates that the metabolic theory of ecology and habitat heterogeneity may be the major underlying ecological mechanisms shaping the biogeographic patterns of soil diazotrophic communities