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Worldwide genetic variation of the IGHV and TRBV immune receptor gene families in humans.
The immunoglobulin heavy variable (IGHV) and T cell beta variable (TRBV) loci are among the most complex and variable regions in the human genome. Generated through a process of gene duplication/deletion and diversification, these loci can vary extensively between individuals in copy number and contain genes that are highly similar, making their analysis technically challenging. Here, we present a comprehensive study of the functional gene segments in the IGHV and TRBV loci, quantifying their copy number and single-nucleotide variation in a globally diverse sample of 109 (IGHV) and 286 (TRBV) humans from over a 100 populations. We find that the IGHV and TRBV gene families exhibit starkly different patterns of variation. In addition to providing insight into the different evolutionary paths of the IGHV and TRBV loci, our results are also important to the adaptive immune repertoire sequencing community, where the lack of frequencies of common alleles and copy number variants is hampering existing analytical pipelines
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
Pre-training and fine-tuning have achieved great success in the natural
language process field. The standard paradigm of exploiting them includes two
steps: first, pre-training a model, e.g. BERT, with a large scale unlabeled
monolingual data. Then, fine-tuning the pre-trained model with labeled data
from downstream tasks. However, in neural machine translation (NMT), we address
the problem that the training objective of the bilingual task is far different
from the monolingual pre-trained model. This gap leads that only using
fine-tuning in NMT can not fully utilize prior language knowledge. In this
paper, we propose an APT framework for acquiring knowledge from the pre-trained
model to NMT. The proposed approach includes two modules: 1). a dynamic fusion
mechanism to fuse task-specific features adapted from general knowledge into
NMT network, 2). a knowledge distillation paradigm to learn language knowledge
continuously during the NMT training process. The proposed approach could
integrate suitable knowledge from pre-trained models to improve the NMT.
Experimental results on WMT English to German, German to English and Chinese to
English machine translation tasks show that our model outperforms strong
baselines and the fine-tuning counterparts
Research on Passenger Flow Control Plans for a Metro Station Based on Social Force Model
To better utilise the service capacity of the limited facilities of a metro station, as well as ensure safety and transport efficiency during peak hours, a large passenger flow control plan is studied through theoretical analysis and numerical simulation. Firstly, by passenger data collection and data analysis, the characteristics of the inbound and outbound passenger flow of a T metro station are analysed. Secondly, AnyLogic evacuation simulation models for the T Station during peak hours, peak hours without/with passenger flow control are established based on real passenger flow data as well as the station structures and layouts by using the AnyLogic software. The results show that there are no obvious congestions in the station hall, and the travel delay is significantly reduced when effective passenger flow control measures are taken. By controlling the speed, direction and movement path of passengers, as well as adjusting the operation of escalators, entrances and automatic ticket-checking machines, passenger flow can become more orderly, transport efficiency can also be improved, and congestion in the station can be well mitigated
Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM
Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station
Robustness of quantum correlations against decoherence
We study dynamics of nonclassical correlations by exactly solving a model
consisting of two atomic qubits with spontaneous emission. We find that the
nonclassical correlations defined by different measures give different
qualitative characterizations of those correlations. The relative behaviors of
those correlation measures are presented explicitly for various quantum states
in the two-qubit atomic system. In particular, we find that the robustness of
quantum correlations can be greatly enhanced by performing appropriate local
unitary operations on the initial state of the system.Comment: 7 pages, 6 figure
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