Quarkyonic matter and quarkyonic stars in an extended RMF model

By combining RMF models and equivparticle models with density-dependent quark masses, we construct explicitly ``a quark Fermi Sea'' and ``a baryonic Fermi surface'' to model the quarkyonic phase, where baryons with momentums ranging from zero to Fermi momentums are included. The properties of nuclear matter, quark matter, and quarkyonic matter are then investigated in a unified manner, where quarkyonic matter is more stable and energy minimization is still applicable to obtain the microscopic properties of dense matter. Three different covariant density functionals TW99, PKDD, and DD-ME2 are adopted in our work, where TW99 gives satisfactory predictions for the properties of nuclear matter both in neutron stars and heavy-ion collisions and quarkyonic transition is unfavorable. Nevertheless, if PKDD with larger slope of symmetry energy $L$ or DD-ME2 with larger skewness coefficient $J$ are adopted, the corresponding EOSs are too stiff according to both experimental and astrophysical constraints. The situation is improved if quarkyonic transition takes place, where the EOSs become softer and can accommodate various experimental and astrophysical constraints

Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks

Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics. As a result, these models suffer from accuracy decay over a long time and thus require frequent calibration. In this work, we address this issue by formulating BP estimation as a sequence prediction problem in which both the input and target are temporal sequences. We propose a novel deep recurrent neural network (RNN) consisting of multilayered Long Short-Term Memory (LSTM) networks, which are incorporated with (1) a bidirectional structure to access larger-scale context information of input sequence, and (2) residual connections to allow gradients in deep RNN to propagate more effectively. The proposed deep RNN model was tested on a static BP dataset, and it achieved root mean square error (RMSE) of 3.90 and 2.66 mmHg for systolic BP (SBP) and diastolic BP (DBP) prediction respectively, surpassing the accuracy of traditional BP prediction models. On a multi-day BP dataset, the deep RNN achieved RMSE of 3.84, 5.25, 5.80 and 5.81 mmHg for the 1st day, 2nd day, 4th day and 6th month after the 1st day SBP prediction, and 1.80, 4.78, 5.0, 5.21 mmHg for corresponding DBP prediction, respectively, which outperforms all previous models with notable improvement. The experimental results suggest that modeling the temporal dependencies in BP dynamics significantly improves the long-term BP prediction accuracy.Comment: To appear in IEEE BHI 201

SmartUnit: Empirical Evaluations for Automated Unit Testing of Embedded Software in Industry

In this paper, we aim at the automated unit coverage-based testing for embedded software. To achieve the goal, by analyzing the industrial requirements and our previous work on automated unit testing tool CAUT, we rebuild a new tool, SmartUnit, to solve the engineering requirements that take place in our partner companies. SmartUnit is a dynamic symbolic execution implementation, which supports statement, branch, boundary value and MC/DC coverage. SmartUnit has been used to test more than one million lines of code in real projects. For confidentiality motives, we select three in-house real projects for the empirical evaluations. We also carry out our evaluations on two open source database projects, SQLite and PostgreSQL, to test the scalability of our tool since the scale of the embedded software project is mostly not large, 5K-50K lines of code on average. From our experimental results, in general, more than 90% of functions in commercial embedded software achieve 100% statement, branch, MC/DC coverage, more than 80% of functions in SQLite achieve 100% MC/DC coverage, and more than 60% of functions in PostgreSQL achieve 100% MC/DC coverage. Moreover, SmartUnit is able to find the runtime exceptions at the unit testing level. We also have reported exceptions like array index out of bounds and divided-by-zero in SQLite. Furthermore, we analyze the reasons of low coverage in automated unit testing in our setting and give a survey on the situation of manual unit testing with respect to automated unit testing in industry.Comment: In Proceedings of 40th International Conference on Software Engineering: Software Engineering in Practice Track, Gothenburg, Sweden, May 27-June 3, 2018 (ICSE-SEIP '18), 10 page

Beam Position Determination using Tracks

Track-based algorithms to determine the LHC beam position and profile at the CMS collision point are described. Only track information is used and no reconstruction of the primary event vertex is required. With only about thousand tracks, a statistical precision of 2 microns for the transverse beam position is achieved, assuming a well aligned detector. The algorithms are simple and fast, and can be used to monitor the beam in real time. A method to determine the track impact parameter resolution using the beam position and beam width calculation is also presented

Competence Set Expansion Decision-making Analysis Based on Important Degree Coefficient

The talented person competence is cultivated and expanded to the actual requisite competence set that has many competence subsets，then carrying on the arrangement of these many competences subset according to its important degree coefficient for providing powerful basis to get the optimal expansion process of expanding from the obtained competence set Sk(E) to the actual requisite competence set Tr(E)．This article uses the fuzzy thought to get various competences subset important degree coefficient in the actual requisite competence set Tr(E)． Key words: Expansion of competence set, Important degree coefficient, Decision analysis Résumé: La compétence douée de personne est cultivée et étendue à l’ensemble requis réel de compétence qui comprend beaucoup de sous-ensembles. On procède ensuite à la gestion de ces sous-ensembles de compétence selon leur coefficient de degré important pour fournir la base puissante, dans le but d’obtenir le processus d’expansion optimal de l’ensemble obtenu de compétence Sk(E) à l’ensemble requis réel de compétence Tr(E). Le présent article utilise des pensées brouillées pour obtenir le coefficient de degré important de l’ensemble de compétences variées dans l’ensemble requis réel de compétence Tr(E). Mots-Clés: expansion de l’ensemble de compétence, coefficient de degré important, analyse de décisio