Modeling the High-Pressure Solid and Liquid Phases of Tin from Deep Potentials with ab initio Accuracy

Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-based deep potentials for Sn with pressures ranging from 0 to 50 GPa and temperatures ranging from 0 to 2000 K. In particular, we find the deep potential, which is obtained by training the ab initio data from density functional theory calculations with the state-of-the-art SCAN exchange-correlation functional, is suitable to characterize high-pressure phases of Sn. We systematically validate several structural and elastic properties of the {\alpha} (diamond structure), {\beta}, bct, and bcc structures of Sn, as well as the structural and dynamic properties of liquid Sn. The thermodynamics integration method is further utilized to compute the free energies of the {\alpha}, {\beta}, bct, and liquid phases, from which the deep potential successfully predicts the phase diagram of Sn including the existence of the triple-point that qualitatively agrees with the experiment

ESSM: An Extractive Summarization Model with Enhanced Spatial-Temporal Information and Span Mask Encoding

Extractive reading comprehension is to extract consecutive subsequences from a given article to answer the given question. Previous work often adopted Byte Pair Encoding (BPE) that could cause semantically correlated words to be separated. Also, previous features extraction strategy cannot effectively capture the global semantic information. In this paper, an extractive summarization model is proposed with enhanced spatial-temporal information and span mask encoding (ESSM) to promote global semantic information. ESSM utilizes Embedding Layer to reduce semantic segmentation of correlated words, and adopts TemporalConvNet Layer to relief the loss of feature information. The model can also deal with unanswerable questions. To verify the effectiveness of the model, experiments on datasets SQuAD1.1 and SQuAD2.0 are conducted. Our model achieved an EM of 86.31% and a F1 score of 92.49% on SQuAD1.1 and the numbers are 80.54% and 83.27% for SQuAD2.0. It was proved that the model is effective for extractive QA task

Is ultrasound combined with computed tomography useful for distinguishing between primary thyroid lymphoma and Hashimoto’s thyroiditis?

Introduction: The aim of the study is to investigate the usefulness of ultrasound combined with computed tomography (CT) for distinguishing between primary thyroid lymphoma (PTL) and Hashimoto’s thyroiditis (HT). Material and methods: The investigation was conducted retrospectively in 80 patients from January 2000 to July 2018. All patients underwent pathological tests to be classified into one of two groups: PTL group and HT group. The cut-off value of CT density was determined using receiver-operating characteristic (ROC) curve analysis. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of diagnosis for thyroid by CT alone, ultrasound alone, and the combination of CT plus ultrasound were calculated. Results: Of the 80 study patients, 27 patients were PTL and 53 patients were HT. Mean CT density had a sensitivity of 90.6% and a specificity of 88.9% at a cut-off value of 53.5 HU, with area under the curve (AUC) 0.88. Ultrasound combined with CT had the highest specificity, accuracy, and PPV compared with CT alone and ultrasound alone (p value < 0.05). Conclusions: Features such as extremely hypoechogenicity, enhanced posterior echo, cervical lymphadenopathy in ultrasound image, and linear high-density strand signs, and very low density in CT imaging have high sensitivity and specificity in thyroid lymphoma. Therefore, ultrasound combined with CT may be useful for distinguishing between PTL and HT.

Migration governance and agrarian and rural development: Comparative lessons from China, Ethiopia, Kyrgyzstan, Moldova, Morocco, Nepal and Thailand

The purpose of this policy brief is to draw together key comparative lessons on different types of migration governance interventions in the AGRUMIG project research regions and examine how they support positive feedback loops between migration and agrarian and rural development. This exploration offers stories of success and omission. Moving beyond the elusive triple-win situation on the benefits of migration for destination and origin countries, migrants themselves and the highly politicized domain of the migration-development nexus, our point of departure is that there are vital prospects for augmenting the positive impacts of migration for societies globally. This brief focuses on how migration governance interventions are potentially useful in maximizing the gains between migration and agrarian development in the sending communities in China, Ethiopia, Kyrgyzstan, Moldova, Morocco, Nepal and Thailand