Stan is a new Bayesian statistical software program that implements the powerful and efficient Hamiltonian Monte Carlo (HMC) algorithm. To date there is not a source that systematically provides Stan ...code for various item response theory (IRT) models. This article provides Stan code for three representative IRT models, including the three-parameter logistic IRT model, the graded response model, and the nominal response model. We demonstrate how IRT model comparison can be conducted with Stan and how the provided Stan code for simple IRT models can be easily extended to their multidimensional and multilevel cases.
Summary The Lancet Countdown: tracking progress on health and climate change is an international, multidisciplinary research collaboration between academic institutions and practitioners across the ...world. It follows on from the work of the 2015 Lancet Commission, which concluded that the response to climate change could be “the greatest global health opportunity of the 21st century”. The Lancet Countdown aims to track the health impacts of climate hazards; health resilience and adaptation; health co-benefits of climate change mitigation; economics and finance; and political and broader engagement. These focus areas form the five thematic working groups of the Lancet Countdown and represent different aspects of the complex association between health and climate change. These thematic groups will provide indicators for a global overview of health and climate change; national case studies highlighting countries leading the way or going against the trend; and engagement with a range of stakeholders. The Lancet Countdown ultimately aims to report annually on a series of indicators across these five working groups. This paper outlines the potential indicators and indicator domains to be tracked by the collaboration, with suggestions on the methodologies and datasets available to achieve this end. The proposed indicator domains require further refinement, and mark the beginning of an ongoing consultation process—from November, 2016 to early 2017—to develop these domains, identify key areas not currently covered, and change indicators where necessary. This collaboration will actively seek to engage with existing monitoring processes, such as the UN Sustainable Development Goals and WHO's climate and health country profiles. The indicators will also evolve over time through ongoing collaboration with experts and a range of stakeholders, and be dependent on the emergence of new evidence and knowledge. During the course of its work, the Lancet Countdown will adopt a collaborative and iterative process, which aims to complement existing initiatives, welcome engagement with new partners, and be open to developing new research projects on health and climate change.
As a special case of machine learning, incremental learning can acquire useful knowledge from incoming data continuously while it does not need to access the original data. It is expected to have the ...ability of memorization and it is regarded as one of the ultimate goals of artificial intelligence technology. However, incremental learning remains a long term challenge. Modern deep neural network models achieve outstanding performance on stationary data distributions with batch training. This restriction leads to catastrophic forgetting for incremental learning scenarios since the distribution of incoming data is unknown and has a highly different probability from the old data. Therefore, a model must be both plastic to acquire new knowledge and stable to consolidate existing knowledge. This review aims to draw a systematic review of the state of the art of incremental learning methods. Published reports are selected from Web of Science, IEEEXplore, and DBLP databases up to May 2020. Each paper is reviewed according to the types: architectural strategy, regularization strategy and rehearsal and pseudo-rehearsal strategy. We compare and discuss different methods. Moreover, the development trend and research focus are given. It is concluded that incremental learning is still a hot research area and will be for a long period. More attention should be paid to the exploration of both biological systems and computational models.
Herein, we describe a simple two‐step approach to prepare nickel phosphide with different phases, such as Ni2P and Ni5P4, to explain the influence of material microstructure and electrical ...conductivity on electrochemical performance. In this approach, we first prepared a Ni–P precursor through a ball milling process, then controlled the synthesis of either Ni2P or Ni5P4 by the annealing method. The as‐prepared Ni2P and Ni5P4 are investigated as supercapacitor electrode materials for potential energy storage applications. The Ni2P exhibits a high specific capacitance of 843.25 F g−1, whereas the specific capacitance of Ni5P4 is 801.5 F g−1. Ni2P possesses better cycle stability and rate capability than Ni5P4. In addition, the Fe2O3//Ni2P supercapacitor displays a high energy density of 35.5 Wh kg−1 at a power density of 400 W kg−1 and long cycle stability with a specific capacitance retention rate of 96 % after 1000 cycles, whereas the Fe2O3//Ni5P4 supercapacitor exhibits a high energy density of 29.8 Wh kg−1 at a power density of 400 W kg−1 and a specific capacitance retention rate of 86 % after 1000 cycles.
Ni2P and Ni5P4 with different microstructures and electrical conductivities have been successfully synthesized and both exhibit excellent electrochemical performance. Ni2P exhibits a high specific capacitance of 843.25 F g−1, whereas the specific capacitance of Ni5P4 is 801.5 F g−1. The Fe2O3//Ni2P supercapacitor displays a high energy density of 35.5 Wh kg−1 at a power density of 400 W kg−1 and long cycle stability with a specific capacitance retention rate of 96 % after 1000 cycles (see figure).
Distance metric learning (DML) is a critical factor for image analysis and pattern recognition. To learn a robust distance metric for a target task, we need abundant side information (i.e., the ...similarity/dissimilarity pairwise constraints over the labeled data), which is usually unavailable in practice due to the high labeling cost. This paper considers the transfer learning setting by exploiting the large quantity of side information from certain related, but different source tasks to help with target metric learning (with only a little side information). The state-of-the-art metric learning algorithms usually fail in this setting because the data distributions of the source task and target task are often quite different. We address this problem by assuming that the target distance metric lies in the space spanned by the eigenvectors of the source metrics (or other randomly generated bases). The target metric is represented as a combination of the base metrics, which are computed using the decomposed components of the source metrics (or simply a set of random bases); we call the proposed method, decomposition-based transfer DML (DTDML). In particular, DTDML learns a sparse combination of the base metrics to construct the target metric by forcing the target metric to be close to an integration of the source metrics. The main advantage of the proposed method compared with existing transfer metric learning approaches is that we directly learn the base metric coefficients instead of the target metric. To this end, far fewer variables need to be learned. We therefore obtain more reliable solutions given the limited side information and the optimization tends to be faster. Experiments on the popular handwritten image (digit, letter) classification and challenge natural image annotation tasks demonstrate the effectiveness of the proposed method.
Straw return is an important management tool for tackling and promoting soil nutrient conservation and improving crop yield in Huang-Huai-Hai Plain, China. Although the incorporation of maize straw ...with deep plowing and rotary tillage practices are widespread in the region, only few studies have focused on rotation tillage. To determine the effects of maize straw return on the nitrogen （N） efficiency and grain yield of winter wheat （Triticum aestivum L.）, we conducted experiments in this region for 3 years. Five treatments were tested： （i） rotary tillage without straw return （RT）; （ii） deep plowing tillage without straw return （DT）; （iii） rotary tillage with total straw return （RS）; （iv） deep plowing tillage with total straw return （DS）; （v） rotary tillage of 2 years and deep plowing tillage in the 3rd year with total straw return （TS）. Treatments with straw return increased kernels no. ear-1, thousand-kernel weight （TKW）, grain yields, ratio of dry matter accumulation post-anthesis, and nitrogen （N） efficiency whereas reduced the ears no. ha-1 in the 2011-2012 and 2012-2013 growing seasons. Compared with the rotary tillage, deep plowing tillage significantly increased the grain yield, yield components, total dry matter accumulation, and N efficiency in 2013-2014. RS had significantly higher straw N distribution, soil inorganic nitrogen content, and soil enzymes activities in the 0-10 cm soil layer compared with the DS and TS. However, significantly lower values were ob- served in the 10-20 and 20-30 cm soil layers. TS obtained approximately equal grain yield as DS, and it also reduced the resource costs. Therefore, we conclude that TS is the most economical method for increasing grain yield and N efficiency of winter wheat in Huang-Huai-Hai Plain.
Programmed cell death ligand 1 (PD‐L1), inducing T cell exhaustion to facilitate immune escape of tumor cells, is upregulated by interleukin 6 (IL‐6) in T cell lymphoma and ovarian cancer. The ...purpose of this study is to investigate the expression of IL‐6 and PD‐L1 in thyroid cancer, and whether IL‐6 regulates PD‐L1 expression. As a result, IL‐6 and PD‐L1 were highly expressed in thyroid cancer tissues. Multivariate logistic analysis showed that tumor size, distant metastasis, and risk stratification were significantly associated with IL‐6 expression (P < .05), and multifocality, lymph node metastasis, distant metastasis, risk stratification, and IL‐6 expression were identified as the independent predictors of PD‐L1 expression (P < .05). The invasiveness of thyroid cancer was significantly enhanced after IL‐6 treatment or PD‐L1 overexpression. PD‐L1 positive rate correlated with IL‐6 expression in cancer tissues (P < .001), and after IL‐6 treatment, the PD‐L1 expression in TPC‐1 and BCPAP significantly increased. The mitogen‐activated protein kinase pathway (MAPK) and the Janus‐activated kinase (JAK)–signal transducers and activators of transcription 3 (STAT3) signaling pathways were activated by IL‐6, and the IL‐6–induced PD‐L1 expression decreased after treatment with these two signaling pathway inhibitors. Knockdown of transcription factors c‐Jun and stat3 suppressed the expression of PD‐L1 induced by IL‐6, and these two factors could bind to PD‐L1 gene promoter directly and promote its transcription. It is concluded that IL‐6 and PD‐L1 are overexpressed in thyroid cancer and are related to tumor invasiveness. IL‐6 upregulates PD‐L1 expression through the MAPK and JAK‐STAT3 signaling pathways, which function via transcription factors c‐Jun and stat3.
IL‐6 and PD‐L1 are highly expressed in thyroid cancer and correlate with disease aggressiveness. IL‐6 activates the MAPK and JAK‐STAT3 signaling pathways in thyroid cancer. In addition, IL‐6 promotes PD‐L1 transcription through the MAPK and JAK‐STAT3 signaling pathways, which function via transcription factors c‐Jun and stat3.
Under complex geostress caused by long-term geological evolution, approximately parallel bedding structures are normally created in rocks due to sedimentation or metamorphism. This type of rock is ...known as transversely isotropic rock (TIR). Due to the existence of bedding planes, the mechanical properties of TIR are quite different from those of relatively homogeneous rocks. The purpose of this review is to discuss the research progress into the mechanical properties and failure characteristics of TIR and to explore the influence of the bedding structure on the rockburst characteristics of the surrounding rocks. First, the P-wave velocity characteristics of the TIR is summarized, followed by the mechanical properties (e.g., the uniaxial compressive strength, the triaxial compressive strength, and tensile strength) and the related failure characteristics of the TIR. The strength criteria of the TIR under triaxial compression are also summarized in this section. Second, the research progress of the rockburst tests on the TIR is reviewed. Finally, six prospects for the study of the transversely isotropic rock are presented: (1) measuring the Brazilian tensile strength of the TIR; (2) establishing the strength criteria for the TIR; (3) revealing the influence mechanism of the mineral particles between the bedding planes on rock failure from the microscopic point of view; (4) investigating the mechanical properties of the TIR in complex environments; (5) experimentally investigating the rockburst of the TIR under the stress path of "the three-dimensional high stress + internal unloading + dynamic disturbance"; and (6) studying the influence of the bedding angle, thickness, and number on the rockburst proneness of the TIR. Finally, some conclusions are summarized.
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