A class of super Heisenberg-Virasoro algebras

In this paper, we introduce a class of Heisenberg-Virasoro Lie conformal superalgebras $\mathfrak{s}$ by using the conformal modules of Heisenberg-Virasoro Lie conformal algebras. A class of super Heisenberg-Virasoro algebras of Ramond type $\S$ are defined by the formal distribution Lie superalgebras of $\mathfrak{s}$. Then we construct a class of simple $\S$-modules, which are induced from simple modules of the finite-dimensional solvable Lie superalgebras. These modules are isomorphic to simple restricted $\S$-modules, and include the highest weight modules, Whittaker modules and high order Whittaker modules and so on. As a byproduct, we present a class of subalgebras of $\S$, which are isomorphic to the super Heisenberg-Virasoro algebras of Neveu-Schwarz type

Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks

The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we therefore adopt coordination-guided convolutional layers (CoordConvs) that generate additional feature maps of the positional information of pulmonary lobes. The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0.947 $\pm$ 0.044.Comment: ISBI 2019 (Oral

Non-weight modules over the super-BMS3_3 algebra

In the present paper, a class of non-weight modules over the super-BMS$_3$ algebras $\S^{\epsilon}$ ($\epsilon=0$ or $\frac{1}{2}$) are constructed. These modules when regarded as $\S^{0}$-modules and further restricted as modules over the Cartan subalgebra $\mathfrak{h}$ are free of rank $1$, while when regarded as $\S^{\frac{1}{2}}$-modules and further restricted as modules over the Cartan subalgebra $\mathfrak{H}$ are free of rank $2$. We determine the necessary and sufficient conditions for these modules being simple, as well as determining the necessary and sufficient conditions for two $\S^{\epsilon}$-modules being isomorphic. At last, we present that these modules constitute a complete classification of free $U(\mathfrak{h})$-modules of rank $1$ over $\S^{0}$, and also constitute a complete classification of free $U(\mathfrak{H})$-modules of rank $2$ over $\S^{\frac{1}{2}}$.Comment: arXiv admin note: text overlap with arXiv:1906.07129 by other author

Equipments for Crop Protection:Standardization Development in China

 The history of standardization for crop protection equipments was reviewed to analyze the trends of standards preparation in this paper. The currently active standards were firstly reviewed by their attributes to present the general state of art. The trends of standard preparation, through which the overall development of crop protection equipments are reflected, were interpreted by descriptive items. Finally the future development was predicted as suggestions for decision-making in policy constitution

Origin of the Coulomb Pseudopotential

We address the outstanding problem of electron pairing in the presence of strong Coulomb repulsion at small to moderate values of the Coulomb parameter, $r_s \lesssim 2$, and demonstrate that the pseudopotential framework is fundamentally biased and uncontrolled. Instead, one has to break the net result into two distinctively different effects: the Fermi liquid renormalization factor and the change in the effective low-energy coupling. The latter quantity is shown to behave non-monotonically with an extremum at $r_s\approx 0.75$. Within the random-phase approximation, Coulomb interaction starts to enhance the effective pairing coupling at $r_s >2$, and the suppression of the critical temperature is entirely due to the renormalized Fermi liquid properties. Leading vertex corrections change this picture only quantitatively. Our results call for radical reconsideration of the widely accepted repulsive pseudopotential approach and show the need for precise microscopic treatment of Coulomb interactions in the problem of superconducting instability

STEM teaching for the Internet of Things maker course: a teaching model based on the iterative loop.

As the key technology for 5G applications in the future, the Internet of Things (IoT) is developing rapidly, and the demand for the cultivation of engineering talents in the IoT is also expanding. The rise of maker education has brought new teaching inspiration for cultivating innovative technical talents in the IoT. In the IoT maker course, teaching problems include the lack of adequate teaching models, emphasis on products but less emphasis on theory, and letting students imitate practice. Focusing on these problems, this paper proposes a new Science, Technology, Engineering, and Mathematics (STEM) teaching model called Propose, Guide, Design, Comment, Implement, Display and Evaluate (PGDCIDE) for the IoT maker course. The PGDCIDE teaching model is based on STEM teaching and Kolodner's design-based scientific inquiry learning cycle model, and realizes the combination of "theory, practice, and innovation." Finally, this paper designs the IoT maker course to practice the PGDCIDE model. The practical results indicate that students significantly improved their emotional level, knowledge level, and innovation level after studying the course. Therefore, the PGDCIDE teaching model proposed in this paper can improve the effectiveness of the IoT maker course teaching and is conducive to the cultivation of students' sustainable ability in engineering education. It has reference significance for the application of maker courses in engineering education practice

SSAH: Semi-supervised Adversarial Deep Hashing with Self-paced Hard Sample Generation

Deep hashing methods have been proved to be effective and efficient for large-scale Web media search. The success of these data-driven methods largely depends on collecting sufficient labeled data, which is usually a crucial limitation in practical cases. The current solutions to this issue utilize Generative Adversarial Network (GAN) to augment data in semi-supervised learning. However, existing GAN-based methods treat image generations and hashing learning as two isolated processes, leading to generation ineffectiveness. Besides, most works fail to exploit the semantic information in unlabeled data. In this paper, we propose a novel Semi-supervised Self-pace Adversarial Hashing method, named SSAH to solve the above problems in a unified framework. The SSAH method consists of an adversarial network (A-Net) and a hashing network (H-Net). To improve the quality of generative images, first, the A-Net learns hard samples with multi-scale occlusions and multi-angle rotated deformations which compete against the learning of accurate hashing codes. Second, we design a novel self-paced hard generation policy to gradually increase the hashing difficulty of generated samples. To make use of the semantic information in unlabeled ones, we propose a semi-supervised consistent loss. The experimental results show that our method can significantly improve state-of-the-art models on both the widely-used hashing datasets and fine-grained datasets