An ANNs-Based Method for Automated Labelling of Schematic Metro Maps

Schematic maps are popular for representing transport networks. In the last two decades, some researchers have been working toward automated generation of network layouts (i.e., the network geometry of schematic maps), while automated labelling of schematic maps is not well considered. The descriptive-statistics-based labelling method, which models the labelling space by defining various station-based line relations in advance, has been specially developed for schematic maps. However, if a certain station-based line relation is not predefined in the database, this method may not be able to infer suitable labelling positions under this relation. It is noted that artificial neural networks (ANNs) have the ability to infer unseen relations. In this study, we aim to develop an ANNs-based method for the labelling of schematic metro maps. Samples are first extracted from representative schematic metro maps, and then they are employed to train and test ANNs models. Five types of attributes (e.g., station-based line relations) are used as inputs, and two types of attributes (i.e., directions and positions of labels) are used as outputs. Experiments show that this ANNs-based method can generate effective and satisfactory labelling results in the testing cases. Such a method has potential to be extended for the labelling of other transport networks.

Exploration on Machine Learning Layout Generation of Chinese Private Garden in Southern Yangtze

Machine learning has been proved to be feasible and reasonable in architectural field by extensive researches recently, whereas its potential is far from being tapped. Previous studies show that the training of GAN by labelling can enable a computer to grasp interrelationship of spatial elements and logical relationship between spatial elements and boundary. This study set the learning object as layout of private gardens in southern Yangtze with higher complexity. Chinese scholars usually analyse private garden layout based on their observation and experience. In this paper, based on Pix2Pix model, we enable a computer to generate private garden layout plan for given site conditions by learning classic cases of traditional Chinese private gardens. Through the experiment, taking Lingering garden as example, we continuously adjust the labelling method to improve learning effect. The finally trained model can quickly generate private garden layout and aid designers to complete scheme design with private garden element corpus. In addition, the working process of training GAN enables us to discover and verify some private garden layout rules that have not been paid attention to.

Substrate Specificity of SARS-CoV-2 Nsp10-Nsp16 Methyltransferase

The ongoing COVID-19 pandemic exemplifies the general need to better understand viral infections. The positive single-strand RNA genome of its causative agent, the SARS coronavirus 2 (SARS-CoV-2), encodes all viral enzymes. In this work, we focused on one particular methyltransferase (MTase), nsp16, which, in complex with nsp10, is capable of methylating the first nucleotide of a capped RNA strand at the 2′-O position. This process is part of a viral capping system and is crucial for viral evasion of the innate immune reaction. In light of recently discovered non-canonical RNA caps, we tested various dinucleoside polyphosphate-capped RNAs as substrates for nsp10-nsp16 MTase. We developed an LC-MS-based method and discovered four types of capped RNA (m7Gp3A(G)- and Gp3A(G)-RNA) that are substrates of the nsp10-nsp16 MTase. Our technique is an alternative to the classical isotope labelling approach for the measurement of 2′-O-MTase activity. Further, we determined the IC50 value of sinefungin to illustrate the use of our approach for inhibitor screening. In the future, this approach may be an alternative technique to the radioactive labelling method for screening inhibitors of any type of 2′-O-MTase.

A Robust Data-Driven Method for Multiseasonality and Heteroscedasticity in Time Series Preprocessing

Internet of Things (IoT) is emerging, and 5G enables much more data transport from mobile and wireless sources. The data to be transmitted is too much compared to link capacity. Labelling data and transmit only useful part of the collected data or their features is a promising solution for this challenge. Abnormal data are valuable due to the need to train models and to detect anomalies when being compared to already overflowing normal data. Labelling can be done in data sources or edges to balance the load and computing between sources, edges, and centres. However, unsupervised labelling method is still a challenge preventing to implement the above solutions. Two main problems in unsupervised labelling are long-term dynamic multiseasonality and heteroscedasticity. This paper proposes a data-driven method to handle modelling and heteroscedasticity problems. The method contains the following main steps. First, raw data are preprocessed and grouped. Second, main models are built for each group. Third, models are adapted back to the original measured data to get raw residuals. Fourth, raw residuals go through deheteroscedasticity and become normalized residuals. Finally, normalized residuals are used to conduct anomaly detection. The experimental results with real-world data show that our method successfully increases receiver-operating characteristic (AUC) by about 30%.

A Connected Component Labelling algorithm for multi-pixel per clock cycle video stream

This work describes the hardware implementation of a connected component labelling (CCL) module in reprogammable logic. The main novelty of the design is the ``full'', i.e. without any simplifications, support of a 4 pixel per clock format (4 ppc) and real-time processing of a 4K/UltraHD video stream (3840 x 2160 pixels) at 60 frames per second. To achieve this, a special labelling method was designed and a functionality that stops the input data stream in order to process pixel groups which require writing more than one merger into the equivalence table. The proposed module was verified in simulation and in hardware on the Xilinx Zynq Ultrascale+ MPSoC chip on the ZCU104 evaluation board.

A Connected Component Labelling algorithm for multi-pixel per clock cycle video stream

This work describes the hardware implementation of a connected component labelling (CCL) module in reprogammable logic. The main novelty of the design is the ``full'', i.e. without any simplifications, support of a 4 pixel per clock format (4 ppc) and real-time processing of a 4K/UltraHD video stream (3840 x 2160 pixels) at 60 frames per second. To achieve this, a special labelling method was designed and a functionality that stops the input data stream in order to process pixel groups which require writing more than one merger into the equivalence table. The proposed module was verified in simulation and in hardware on the Xilinx Zynq Ultrascale+ MPSoC chip on the ZCU104 evaluation board.

Efficient genetic transformation method for Eucalyptus genome editing

Plantation forestry of Eucalyptus urophylla × Eucalyptus grandis supplies high-quality raw material for pulp, paper, wood, and energy and thereby reduces the pressures on native forests and their associated biodiversity. Nevertheless, owing to the heterozygosity of the E. urophylla × E. grandis genetic background, germplasm improvement by crossbreeding tends to be inefficient. As an alternative approach, genetic engineering of Eucalyptus can be used to effectively improve germplasm resources. From a strategic standpoint, increasing the plantation productivity and wood quality by transgenic technology has become increasingly important for forest industry. In this study, we established a fluorescence labelling method using CRISPR/Cas9 technology to obtain positive transformed progenies. The positive transformed progenies were easily obtained from the genetically modified population via fluorescence screening. This system can be used as a plant genome site-specific editing tool and may be useful for improving Eucalyptus genetic resources.

Yield Advantage of a Maize-peanut Intercropping System

Maize-peanut intercropping is an important element of China’s agricultural planting model, as it confers ecological benefits, promotes species diversity, and increases economic efficiency and yield. The aim of this study was to explore the yield differences between intercropping and monoculture, and to determine the mechanism underlying the high yield efficiency of the intercropping system using the 13C isotope tracer labelling method. The early maturing corn hybrid Denghai 618 and the early maturing and high-yielding peanut variety Huayu 22 were used as test materials. Three kinds of planting methods were employed, i.e. the sole maize (SM), the sole peanut (SP) and maize–peanut intercropping (intercropped maize, IM; intercropped peanut, IP), for two consecutive years. IM increased yield by 59.7% and 62.3% comparing with SM in 2015 and 2016, respectively. IP reduced yield by 31.3% and 32.3% comparing with SP in 2015 and 2016, respectively. IM significantly increased the photosynthetic rate, leaf area, 13C assimilation distribution, and dry matter accumulation of summer maize, which led to an increase in kernel number, resulting in an increased yield. The decrease in intercropped peanut yield was mainly caused by a decrease in the percent of plump pod and number of pods per plant. The decrease in peanut yield did not affect the production of intercropping, because of the large intercropping advantage and land equivalence ratio. Maize-peanut intercropping provided greater economic benefits than monoculture. These results showed the utility of the peanut-maize intercropping model.