Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Research on Consumers’ Preferences for the Self-Service Mode of Express Cabinets in Stations Based on the Subway Distribution to Promote Sustainability

With the explosive growth in the express delivery business, last-mile delivery issues have come to the forefront in China. Subway-based distribution has been demonstrated and practiced. The self-service mode of express cabinets in stations based on the subway distribution can effectively reduce the last-mile delivery costs, increase the utilization rate of public transportation resources, and reduce traffic congestion and carbon emissions. This paper designed self–service mode of express cabinets in stations and discussed the feasibility by investigating consumers’ preferences. The consumers’ preferences and influencing factors were examined by using the multicategorical logit model. The results show that consumers’ gender, education level and number of online purchases per month have an impact on consumers’ preferences. The majority of consumers are willing to actively engage in green consumer behavior. Meanwhile, consumers are more concerned about whether the express mode is convenient to conduct and the queuing of an express cabinet. Some suggestions and recommendations on promoting this self-service mode were put forward, such as pushing different advertisements for different groups of consumers, designing efficient and multi-function express cabinets, and adopting a reward system. This research provides guidance for decision making regarding the promotion of a new self–service mode based on the subway distribution, which can promote sustainable consumption and improve the efficient operation of urban last-mile delivery and the low-carbon development of urban transportation. Document type: Articl

Digital control of multistep hydrothermal synthesis by using 3D printed reactionware for the synthesis of metal–organic frameworks

Hydrothermal‐synthesis‐based reactions are normally single step owing to the difficulty of manipulating reaction mixtures at high temperatures and pressures. Herein we demonstrate a simple, cheap, and modular approach to the design reactors consisting of partitioned chambers, to achieve multi‐step synthesis under hydrothermal conditions, in digitally defined reactionware produced by 3D printing. This approach increases the number of steps that can be performed sequentially and allows an increase in the options available for the control of hydrothermal reactions. The synthetic outcomes of the multi‐stage reactions can be explored by varying reaction compositions, number of reagents, reaction steps, and reaction times, and these can be tagged to the digital blueprint. To demonstrate the potential of this approach a series of polyoxometalate (POM)‐containing metal–organic frameworks (MOFs) unavailable by “one‐pot” methods were prepared as well as a set of new MOFs

Efficient methods for multiple types of precise gene-editing in Chlamydomonas

Precise gene-editing using CRISPR/Cas9 technology remains a long-standing challenge, especially for genes with low expression and no selectable phenotypes in Chlamydomonas reinhardtii, a classic model for photosynthesis and cilia research. Here, we developed a multi-type and precise genetic manipulation method in which a DNA break was generated by Cas9 nuclease and the repair was mediated using a homologous DNA template. The efficacy of this method was demonstrated for several types of gene editing, including inactivation of two low-expression genes (CrTET1 and CrKU80), the introduction of a FLAG-HA epitope tag into VIPP1, IFT46, CrTET1 and CrKU80 genes, and placing a YFP tag into VIPP1 and IFT46 for live-cell imaging. We also successfully performed a single amino acid substitution for the FLA3, FLA10 and FTSY genes, and documented the attainment of the anticipated phenotypes. Lastly, we demonstrated that precise fragment deletion from the 3'-UTR of MAA7 and VIPP1 resulted in a stable knock-down effect. Overall, our study has established efficient methods for multiple types of precise gene editing in Chlamydomonas, enabling substitution, insertion and deletion at the base resolution, thus improving the potential of this alga in both basic research and industrial applications.</p

RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework

In recent years, remote sensing (RS) vision foundation models such as RingMo have emerged and achieved excellent performance in various downstream tasks. However, the high demand for computing resources limits the application of these models on edge devices. It is necessary to design a more lightweight foundation model to support on-orbit RS image interpretation. Existing methods face challenges in achieving lightweight solutions while retaining generalization in RS image interpretation. This is due to the complex high and low-frequency spectral components in RS images, which make traditional single CNN or Vision Transformer methods unsuitable for the task. Therefore, this paper proposes RingMo-lite, an RS multi-task lightweight network with a CNN-Transformer hybrid framework, which effectively exploits the frequency-domain properties of RS to optimize the interpretation process. It is combined by the Transformer module as a low-pass filter to extract global features of RS images through a dual-branch structure, and the CNN module as a stacked high-pass filter to extract fine-grained details effectively. Furthermore, in the pretraining stage, the designed frequency-domain masked image modeling (FD-MIM) combines each image patch's high-frequency and low-frequency characteristics, effectively capturing the latent feature representation in RS data. As shown in Fig. 1, compared with RingMo, the proposed RingMo-lite reduces the parameters over 60% in various RS image interpretation tasks, the average accuracy drops by less than 2% in most of the scenes and achieves SOTA performance compared to models of the similar size. In addition, our work will be integrated into the MindSpore computing platform in the near future

Periodontal health: A national cross‐sectional study of knowledge, attitudes and practices for the public oral health strategy in China

Aim To assess the status of periodontal health knowledge, attitudes and practices (KAP) among Chinese adults. Materials and Methods A cross‐sectional study was conducted in a nationally representative sample of adults (N = 50,991) aged 20 years or older from ten provinces, autonomous regions, and municipalities. Percentages of Chinese adults with correct periodontal knowledge, positive periodontal attitudes, and practices were estimated. Multiple logistic regression analyses were used to examine the related factors. Results Less than 20% of Chinese adults were knowledgeable about periodontal disease. Very few (2.6%) of Chinese adults use dental floss ≥once a day and undergo scaling ≥once a year and visit a dentist (6.4%) in the case of gingival bleeding. Periodontal health KAP was associated with gender, age, body mass index, marital status, place of residence, education level, income, smoking status, and history of periodontal disease. Conclusions Periodontal health KAP are generally poor among the Chinese adult population. Community‐based health strategies to improve periodontal health KAP need to be implemented. Increasing knowledge of periodontal disease, the cultivation of correct practices in response to gingival bleeding, and the development of good habits concerning the use of dental floss and regular scaling should be public oral health priorities

Effects of the Timing of Note Taking on Repeated Listening among Advanced Chinese Japanese Learners: Focusing on the Timing of Note Taking and Working Memory Span

This study aimed to investigate the effects of the timing of note taking on repeated listening for advanced Chinese learners of Japanese. The two independent variables were participants’ working memory capacity and the timing of the note taking. The main results were as follows: In the free recall test, there was a marginal significance that participants with a low working-memory capacity demonstrated better performance when taking notes during the first trail than participants with a high working-memory capacity. However in the fill-in-blank test, regardless of the timing of the note taking, a difference according to the size of working memory capacity was not found. Further, it was found that taking notes during the first trail left a stronger memory trace, whereas taking notes during the second trail was helpful for the understanding of target passages. These results suggest that learners with a smaller memory span can perform better with note taking and learners with a larger memory span are better of taking notes after the first trail