A Network Celebrity Identification and Evaluation Model Based on Hybrid Trust Relation

Trust-based celebrity user identification is the key to the industry\u27s reputation for electronic word of mouth. However, trust and mistrust are independent and coexistent concepts. In this context, we need to consider the existence of the two kinds of user relations brought about by the impact. This paper analyzes the characteristics of trust and distrust in social networks, and gives formal descriptions of trust networks, untrusted networks, and mixed trust networks. Based on the indicators such as degree distribution, correlation coefficient, and matching coefficient, the structural properties of mixed trust networks are studied. Based on the PageRank algorithm, the HTMM metrics affecting users under the mixed trust network environment are proposed. Finally, the validity of HTMM is verified through a real data set containing trust and distrust. Experimental results show that the results of HTMM\u27s celebrity user identification method still have a low level of trust

Strain distributions in lattice-mismatched semiconductor core-shell nanowires

The authors study the elastic deformation field in lattice-mismatched core-shell nanowires with single and multiple shells. The authors consider infinite wires with a hexagonal cross section under the assumption of translational symmetry. The strain distributions are found by minimizing the elastic energy per unit cell using the finite element method. The authors find that the trace of the strain is discontinuous with a simple, almost piecewise variation between core and shell, whereas the individual components of the strain can exhibit complex variations.Comment: 4 pages, 3 figure

Usability of a Patient Portal and Patient-Perceived Errors in Electronic Health Records: A Survey Study

Patient portals provide access to electronic health records (EHRs) for better en-gaging patients in healthcare services. Patient portals’ usability is important as it directly impacts patients’ experience and acceptance of the portals. Thus, evaluat-ing patient portal usability could help improve the healthcare service quality and patients’ experience. Meanwhile, errors are commonly perceived in EHRs, could lead to further serious problems, and might also negatively influence patients’ evaluation of patient portal usability. However, errors in EHRs have rarely been examined. We aimed to evaluate the subjective usability of a national patient portal, patient-perceived errors in their EHRs, and how their perceptions might be associated with patients’ assessment of patient portal usability. Data were collected from 4719 users of the Finnish national patient portal My Kanta via a three-week online survey in Jan and Feb 2021. Respondents were asked to rate the usability of the patient portal in Likert-scale scores and the average ratings were converted to the System Usability Scale scores for comparison. They were also asked about perceptions of errors and omissions in the records and to rate the seriousness of the most important ones. The average usability scores were then compared and tested. The overall My Kanta usability was evaluated by patients as good (System Usability Scale score: mean 74.3, SD 14.0). Of all these participants, 1664 (35.3%) reported perceiving at least one error in the electronic health records and 200 (14.0%) described the error(s) as very serious. The average usability rating from patients who have perceived errors in EHRs was tested statistically significantly lower than those who haven’t. In conclusion, the usability of My Kanta patient portal was acceptable, but could still be improved. Errors have been perceived in EHRs on My Kanta, could negatively impact users’ assessment of the patient portal usability, and should be reduced for improving user safety and experience

DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training

A standard hardware bottleneck when training deep neural networks is GPU memory. The bulk of memory is occupied by caching intermediate tensors for gradient computation in the backward pass. We propose a novel method to reduce this footprint - Dropping Intermediate Tensors (DropIT). DropIT drops min-k elements of the intermediate tensors and approximates gradients from the sparsified tensors in the backward pass. Theoretically, DropIT reduces noise on estimated gradients and therefore has a higher rate of convergence than vanilla-SGD. Experiments show that we can drop up to 90% of the intermediate tensor elements in fully-connected and convolutional layers while achieving higher testing accuracy for Visual Transformers and Convolutional Neural Networks on various tasks (e.g. classification, object detection).Our code and models are available at https://github.com/chenjoya/dropitComment: 16 pages. DropIT can save memory & improve accuracy, providing a new perspective of dropping in activation compressed training than quantizatio

Transcriptome Analysis and Ultrastructure Observation Reveal that Hawthorn Fruit Softening Is due to Cellulose/Hemicellulose Degradation

Softening, a common phenomenon in many fruits, is a well coordinated and genetically determined process. However, the process of flesh softening during ripening has rarely been described in hawthorn. In this study, we found that ‘Ruanrou Shanlihong 3 Hao’ fruits became softer during ripening, whereas ‘Qiu JinXing’ fruits remained hard. At late developmental stages, the firmness of ‘Ruanrou Shanlihong 3 Hao’ fruits rapidly declined, and that of ‘Qiu JinXing’ fruits remained essentially unchanged. According to transmission electron microscopy (TEM), the middle lamella of ‘Qiu JinXing’ and ‘Ruanrou Shanlihong 3 Hao’ fruit flesh was largely degraded as the fruits matured. Microfilaments in ‘Qiu JinXing’ flesh were arranged close together and were deep in color, whereas those in ‘Ruanrou Shanlihong 3 Hao’ fruit flesh were arranged loosely, partially degraded and light in color. RNA-Seq analysis yielded approximately 46.72 Gb of clean data and 72,837 unigenes. Galactose metabolism and pentose and glucuronate interconversions are involved in cell wall metabolism, play an important role in hawthorn texture. We identified 85 unigenes related to the cell wall between hard- and soft-fleshed hawthorn fruits. Based on data analysis and real-time PCR, we suggest that β-GAL and PE4 have important functions in early fruit softening. The genes Ffase, Gns, α-GAL, PE63, XTH and CWP, which are involved in cell wall degradation, are responsible for the different textures of hawthorn fruits. Thus, we hypothesize that the different textures of ‘Qiu JinXing’ and ‘Ruanrou Shanlihong 3 Hao’ fruits at maturity mainly result from cellulose/hemicelluloses degradation rather than from lamella degradation. Overall, we propose that different types of hydrolytic enzymes in cells interact to degrade the cell wall, resulting in ultramicroscopic Structure changes in the cell wall and, consequently, fruit softening. These results provide fundamental insight regarding the mechanisms by which hawthorn fruits acquire different textures and also lay a solid foundation for further research

Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks

Neural architecture search has attracted wide attentions in both academia and industry. To accelerate it, researchers proposed weight-sharing methods which first train a super-network to reuse computation among different operators, from which exponentially many sub-networks can be sampled and efficiently evaluated. These methods enjoy great advantages in terms of computational costs, but the sampled sub-networks are not guaranteed to be estimated precisely unless an individual training process is taken. This paper owes such inaccuracy to the inevitable mismatch between assembled network layers, so that there is a random error term added to each estimation. We alleviate this issue by training a graph convolutional network to fit the performance of sampled sub-networks so that the impact of random errors becomes minimal. With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates, which consequently leads to better performance of the final architecture. In addition, our approach also enjoys the flexibility of being used under different hardware constraints, since the graph convolutional network has provided an efficient lookup table of the performance of architectures in the entire search space.Comment: Accepted to AAAI 202

The green GDP accounting system based on the BP neural network: an environmental pollution perspective

Introduction: The green GDP accounting system has become the focus of sustainable development, but a comprehensive accounting of environmental pollution cost and resource depletion cost has not yet been formed.Methods: This study measures environmental pollution cost and resource loss cost, and establishes the green GDP accounting system based on the SEEA-2012. To analyze the environmental effects brought by the adoption of green GDP accounting system, a BP neural network model including green GDP, traditional GDP and global climate indicators is constructed to predict the global climate changes.Results: The empirical results show that after the adoption of the green GDP accounting system, the global climate extreme weather can be reduced, the sea level will be lowered, and the climate problem is thus alleviated